{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from FcaNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = fcanet(drop = 0.1).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n"
     ]
    },
    {
     "ename": "OutOfMemoryError",
     "evalue": "CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 19.72 GiB total capacity; 5.15 GiB already allocated; 10.88 MiB free; 5.25 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrainloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m)\u001b[49m  \n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/utils.py:148\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, trainloader, device, testloader, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;66;03m# 前向传播\u001b[39;00m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;66;03m# 只在训练阶段追踪历史\u001b[39;00m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 148\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    149\u001b[0m     _, preds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(outputs, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    150\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/H2ighwayNet.py:70\u001b[0m, in \u001b[0;36mHighwayNet.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     68\u001b[0m out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer1(out)\n\u001b[1;32m     69\u001b[0m out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer2(out)\n\u001b[0;32m---> 70\u001b[0m out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer3\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer4(out)\n\u001b[1;32m     72\u001b[0m out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mavg_pool(out)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/FcaNet.py:132\u001b[0m, in \u001b[0;36mFcaHighwayBlock.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    129\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2(out)\n\u001b[1;32m    130\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn2(out)\n\u001b[0;32m--> 132\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39matt(out)\n\u001b[1;32m    134\u001b[0m down_sample_x \u001b[38;5;241m=\u001b[39m identity\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownsample \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/FcaNet.py:59\u001b[0m, in \u001b[0;36mMultiSpectralAttentionLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m     58\u001b[0m     n,c,h,w \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m---> 59\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdct_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc(y)\u001b[38;5;241m.\u001b[39mview(n, c, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x \u001b[38;5;241m*\u001b[39m y\u001b[38;5;241m.\u001b[39mexpand_as(x)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/deep-learning/FuTong/发起总攻/FcaNet.py:76\u001b[0m, in \u001b[0;36mMultiSpectralDCTLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m     73\u001b[0m     \n\u001b[1;32m     74\u001b[0m     \u001b[38;5;66;03m# print(x.shape)\u001b[39;00m\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;66;03m# print(self.weight.shape)\u001b[39;00m\n\u001b[0;32m---> 76\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\n\u001b[1;32m     78\u001b[0m     result \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39msum(x, dim\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m])\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 32.00 MiB (GPU 0; 19.72 GiB total capacity; 5.15 GiB already allocated; 10.88 MiB free; 5.25 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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hxVOI3pSSB5gh+valhPc4mX4pESxLL2buxnzOHpLi7+pERERERERERKSR+SorqViwAHtsLK5u3Zp8RG9ZbRmr8laxPG85y3OXszJ/JRXuigM6R0ilwfm/+Ri/3DiICvJ2LCYDsIaE4OrZE1evXubSsyfO9u2wWK0ARF58MVlTplCzMZVt/7iO8DPOIP6O27GFhBzE9ZueYRjUpqXhzszE8HgwPB7wejE8XgyPe9dzrwc8nrrnRm0t3uISvIWFeIuKdoTl5vO6yTkbkG25j6DBgwmdOJHQCcfhiIvb71q9JSXkPvEkxR9/DIAtKor4228n7KQT1U7nLxSiN6WkAbD0bchcCsCozjEsSy9mTqpCdBEREZGD9dtvv3H11Vfjcrnqrff5fBx77LEsWrSImpqa3Y4rLy9nzZo1TJs2jXfeeQe7vf5H49raWu68806GDx/O8ccfT1BQ0G7n6NChA1988UXj3pCIiIi0GjVpaWy78ca60b0WpxNXr14E9utHYP9+BPbrhz0hodHCSsMwyCjLqAvMl+ctJ7UoFYP64XeQPYi+sX3pHNEZu9WOxWLBZrFhwYLVYt21GBYSZq+l/TuzcZSZrVm8IYFYsZj7YgHD2Hlx8yqGUW+dxeXC1b37jsC8J4G9euFo27YuMG9IYK9etP/0U/KeeYbCN96k5PPPqVgwn6SHpxI8fFijvFeNzfB4qFyylPKff6bs559xZ+w+Yeqhsrhc2KIisUdEYouKwhYZSW36VqpXrKTyjz+o/OMPch56iMCBAwmbOJHQiRNwxDc8F6NhGJTOmGFOHJpvtpuOOOtvxP3f/2GLiGj02ls6hehNKWmg+Zi5DAyDUV1jeebnVH5PzcfnM7Ba9e2OiIiIyIGqqqri3HPP5b777qu3fsuWLUyZMgWLxcLy5ct3O2706NEYhkFRURHPPfcco0ePrrf9rbfeoqysDLfbzciRI3nrrbd2O8fw4cMb70ZERESkVSn9/gey7rgDX2WlGUoaBt6SEqqWLaNq2bK6/exxcTtC9f4E9u+Hq2dPrIENt/2tdFdSUFVAQXUB+VX59Z7nVOawOn81hdWFux3XJqQN/eP60z+2P/3j+tM5ojM2q22v9Vev30D2/fdTtdQcDBrQpTMJ99xD0JAhB/+mHABrQADxt95K6JgxZN5+B+6MDNL//nciL7qIuJv/ucf3qCl5yyuomDuXsp9nUf7bbHwlJXXbLE4nzo4dsTgcWOx2LDYb2O17eG7DYjf3s4WH7wjII7BHRWGLjMIeFYktMhJrA4M6ANyZmZT+8CNlP/xA1fLlVC1ZQtWSJeQ8/DCBAwYQNmkioRMm4EhMBKB22zay73+AijlzAHB27Eji/fc12Z9tS6QQvSnF9wJbAFSXQGEa/VM6EBJgp7Cilj+zSumdHO7vCkVERERERERE5BAYbje5TzxJ4Y4v4IOGDiX5ySewRUfj3rqVqhUrzGX5CqrXr8eTm0vZzJmUzZxpHm+zUdk+lm1dIlk2LJpN4VV1YXlDE3X+L4fVQa/oXnWheb+4fsQExux3/d7yCvKff57Ct98GrxdLYCCx119H1MUXY3E4Duo9ORRBgwfT8csvyHnscYo/+oiid96hYu5ckh59hMC+fZu8HndODuW//ELZrJ+pXLAAw+2u22aLiCBk9GhCxo0lZORIrMHBTVKTIymJ6Ev/TvSlf8edlUXZjz9S+sOPVC1dWvelTc7URwjs1w9X794Uf/YZRnU1FoeD6GuvIfqKK7Ae4ROH7otC9KZkc0BCH9i+GDKX4YjuxPCO0fy0NofZG/MUoouIiEjzYxjgrmz66zqCQD0YRURE5DAyfD6KPviA0q+/IfT4SUSec84hj2525+ay/eabqVq8BIDoKy4n9qabsOxoG+ds3x5n+/aEn3oqAFn5W1i3YDoFfyzA9udG4tNKiKzwErwpm26bsun2PaxqZ2HmAAtZXS1gs+CyuYgOjDYXVzQxgTFEB0YT44qhW1Q3ekb3xGk78EDUMAzKfviRnKlT8eTkABB63HHE33F73Qhmf7EGB5N4/32Ejh9H1p13Ubt5M1vOO5/oq64k9tprG73PvGEY+EpL8RQU4MnPx5ufT83mzZT/8ivVq1fX29fRri2hY8cROm4sgf371/1Z+4sjMZGoSy4h6pJLcOfkUPbDj5T++ANVS5bWfYEDEDRsGAn33UtAhw5+rbelUIje1JIHmiH69qXQ52+M6hLDT2tzmLsxn3+M7uzv6kRERETqc1fCw0lNf907MsHZNCN3REREpHkxvF4qlyyhbOZPeLKziTz/PIJHjGjUa7izssi6804q5s0HoGrFCgpefY3oyy4j8txzDmoEceUff7Dt5pvx5uWboe8jUwk77ri67TXeGtYWrGVF3oq6Jbcy19zYccdi2OhYHc7YkiQGrCgnZvlW+mw16LPVwBIVSeiZpxN7znk427RphHdhl9qtW8n+90N17T0cKSkk3HUnIcce26jXOVQho0bR8euvyH7w35ROn07Biy9R/ttvJD38MM6UFHMST7e7bjJPw+3eMXHnX9a7PRgetxmS5+WbQXlBPt665wV48/PrjTCvx2IhsF8/QsaOJXTcWLNlSzMd/OGIjyfq4ouIuvgi3Dm5lP00k6rlKwg+aiThp57abOtujhSiN7W6vug7JhftYv6cZvGWIiprPQQ59UciIiIiIiIiIk3LqK0Fm83sz+wHvtpaKhcsMNuazPoZb+Guvt5lM2cSevwk4qdM2eMkifvLMAxKv/2W7AcexFdWhsXlIvKcsyn7aRbu7dvJffxxCl57jahLLyXy/POxhew7TDcMg4I33yTviSfB68XbIZktt5/H3Mh08hY9Ql5lHpnlmawvWo/bVz+YtVlsdI3sSt/YvvSL7Uf/2P60CW1TF266MzMp+uQTij/9FG9ePqWvvkHp628RcswxRJx7DiGjRh3Sn5mvpoaCV1+j4JVXMGprzfYeV15B9FVXYf2fSdubC1tEBMlP/IfQ48aTfd/91Py5ls2nnX5YrmUNDcUeE4M9Ohp7XBxBI4YTOmYM9pj9b4/TXDji44i64AK44AJ/l9IiKbFtask7QvSsFeD10CEmmOSIQLYXV7FwcyFjusX5tz4RERGRv3IEmaPC/XFdEREROewMj4eCV18l/6WXwWIhoEsXArp1xdW1GwFduxLQrSv2yMjDcm1fZSXlc+ZSNnMm5b/+iq+8vG6bLTyckLFjsQQ4Kf74E8q++56K32YTc/31RF104UH15vYUFZF93/2U/fADAK5+fUl65BECOnQg7pZbKPn6G/Jffhl3ejp5Tz5J4euvE/X3S4i88EKqXVYyyjLqluyKbPKr8ikuzGLCexvot8Zsfzenl4VXJmVTs3kabN69hihXVF1g3i+2H72iexG0l889jqQk4iZPJvYf/6Bs1s8UffQhlfMXUP7rr5T/+iuOpCQizj6biDPPwB4b2+A5DJ8PX1kZ3qIivMXFeIqK8BYV4y0soOiTT3BvTQcgeOQI4u++u8W09wibNImgQYPIuvc+yn/+uf5Gu33XhJ47Fhx2c/LOHeutoSHYo2PMkDwmGlt0NPaYWOwx0dhjYrBFR2MNCPDPzUmzoxC9qUV3AWcI1JZD/nos8b0Y1SWGD//IYO7GfIXoIiIi0rxYLGqrIiIi0krVbt1K5r9uq+uRDFC9ahXVq1ZR8pf97HFxBHTrRkDXLri6dTOfd+hwUH2ovSUllP/6K6UzZ1IxZy5GTc2u68TGEnrceEKPO46gwYPrgvLIc84h+/4HqFq+nNzHHqPki8+Jv/tugocO3e/rlv3yC1l334M3Px/sdmKvv47oK67Y1b/absd7wrGUj2hH8fRvCH3/B4Kyisl7+hm2vvws0wfDjMFWKgJ3tb9ok2fwf597SS4EjxXeGm9l4YgIUoLiiAmMIW7HY2xgLHFBcfSI7kGbkDYH1ULD4nAQNmkiYZMmUrN5M8UffUzxF1/gzswkb9o08p57jtCxY7GFh+MtNkNyz45Hb3ExeL17PLc9Npb426cQevzxLa69hz02lpQXnsdXWQkWi/nnabe3uPuQ5q/ZhOiPPPIIt99+O5MnT2batGl73O+TTz7h7rvvZsuWLXTp0oVHH32UE044oekKPVRWKyT2h61zzb7o8b04ekeIPmdjnr+rExEREREREZFWzjAMij/+hJxHHsGoqsIaEkLC3Xfh6tuXmvUbqNmwgeoN66lZvwF3Rgae3Fw8ubl1/bIBsNtxJCWZrUSsVixWC2Axcw+r1QwxLTtfW7BgwfB6qV63DjyeutM4UlIIPe44Qo8bT2C/flis1t3qdfXoQbv336Pkiy/J/c9/qNmYSvrFlxB28snE/+vWPY7ABvCWV5A59SHKP/sCAHfbBNImn8zMxFoKFj1IXmUeuZW5ZJRlUOnZMZm6EywXG4xca+XM3320KTA4ay6c9IePxaPi2XZCf3qluen1zmxsNV6M2CjCHruPh4ceQ4Dt8I9cDujQgfgptxF702RKv/+e4g8+pGrFCsp+/HGvx1mDgrBFRv5liSCgQwciL7oIW0jIYa/7cLIG6VeMcng1ixD9jz/+4OWXX6Zv37573W/evHmcd955TJ06lZNOOon333+f0047jaVLl9K7d+8mqrYRJA8wQ/TMpTDwIo7qFIPFAhtyyskprSY+rHn2nBIRERERERERM4SuWrac4o8+omLhQly9exF+wgmEjB7d7MM8T14eWXfdTflvvwEQNHQoSY9MxZFkTiQe0KEDTJpYt7+3vIKajRuo2bCRmvXr68J1X1kZ7vT0g6ohoEsXMzifcBwB3brt16hhi9VKxJlnEDpuLLlPP03xhx9R+s03lP/yC7E33oDjb6ewomg1S3OWsq1sG3lVeYT8mcFZH2cRW+zDB0wfauHDY/Nw578J+Q1cAwvxwfGkhKaYy+AUfNckYyzZjvPtr2BjKqN+ysYy55e6EfTBI0eQ9J//YI+KOqj34lBYXS4iTjuNiNNOo3rtWspmzgSbDVtkJPa/huURZmBuPYhfDoiIye8henl5ORdccAGvvvoq//73v/e679NPP82kSZO49dZbAXjwwQeZOXMmzz33HC+99FJTlNs46iYXXQZAZLCTvsnhrNhWwpyN+fxtUOPOsCwiIiIiIiIih85bVkbJN99Q/OFH1GzYULe+PDub8p9mYQkMJHTMGMJOPIHgUaOaXWhZOnMm2ffci7eoCIvDQezNNxN1ycUNjv7eyRYSTNCAAQQNGFC3zjAMPFlZuLOywOfD8BlgGGD4MHw+8BmAsWObb8c2A8PnI6Bz50PquW2LiCDx3nuxnTSBrAfux7l+KzkPT2Xra4/w2kQr69tYcHgMzpnt46SFBlYgNxxePcVFfvd4eu5orxIdGE1sYKzZbiUoljYhbUgOTW54JHlHMM68jLJZs8h/4UVq1q4FIPqaq4m94Qa/Tcb6V64ePXD16OHvMkRaLb+H6Ndddx0nnngi48eP32eIPn/+fG6++eZ66yZOnMiXX355GCs8DHZOLpq9Gjw1YA/g6C4xrNhWwtyNeQrRRURERERERJqRqlWrKf74I0q+nY5RVQWAxeUi7PjjCZ04gaplyymdMQN3RgalM2ZQOmMG1tBQQsePJ+yEEwgePuygJsJsLN7ycnIenkrJ558DENC9O0mPPYqra9eDOp/FYsGRlFQ3er0p5FflszhnMUuyl7AkdwkbizZiOd1g7HIr5//qo12uwYPveNk8vC3xmVUEpZstc62nTKD/lCm8E5lwSH2yLVYrYccdR+j48VTOn4/FFUjQwAH7PlBEWgW/hugffvghS5cu5Y8//tiv/bOzs4mPj6+3Lj4+nuzs7D0eU1NTQ81fJqkoLS09uGIbU0Q7CIyCqkLIWQ3JgxjVJZbnf9nE3NR8fD4Dq1UTIIiIiIjsj/DwcL799lu+/fbb3bZNnDiR4uJiBg8e3OCxVquVNm3acMsttzS4/Y477iAwMJDVq1c3eI4+ffocWvEiItJs+SoqKJkxg+IPP6J6zZq69c5OnYg85xzCTz0FW3g4AKGjRxN702SqV6+mdPoMSr/7Dk9ODiVffEHJF19gi4wkdOIEwk44wZwwcy8jvxtb5eLFZN42Bff27WCxEH3F5cTccEOzGyW/k9vnJqs8i62lW0kvS2dj0UaW5CxhS+mW3fbtENGJqHMGUXBJd6I/WkTtl9PpsMBsMWOLjibxwQcIHTu2UeuzWCwEjxzZqOcUkebPbyF6RkYGkydPZubMmbhch68H+NSpU7n//vsP2/kPisVijkZP/cmcXDR5EAPbRhLktJFfXsu67DJ6JoX5u0oRERGRFmHEiBEsXrz4oI+//vrruf766/e6z6GcX0REWpbq9esp/ugjSr76Gl9FBQAWh4PQiROJPPccAgcNanBEs8ViIbBPHwL79CHuX7dStXSpOSr9+x/wFhZS/OFHFH/4Efa4OIJHjsQeG4s9JhpbVHT9x4iIRmkP4qutJf+ZZyh4/Q0wDBzJySQ9+ghBe/hiuSm5fW4yyzPZWrqVjLKMusA8vTSdzPJMvIZ3t2MsWOgW1Y1B8YMYFD+IgXEDiQ6M3rXDgLOpOvdCcp+ahj0+jvgpU/zSp1xEWie/hehLliwhNzeXgQMH1q3zer3Mnj2b5557jpqaGmz/85dGQkICOTk59dbl5OSQkJCwx+vcfvvt9VrAlJaWkpKS0kh3cQiSdoToO/qiO+1WhneM5ud1uczZmKcQXURERERERKQJuXNzyZoyhYp58+vWOdq1JfLscwg/43TskZH7fS6L1UrQ4MEEDR5M/B13ULFwIaUzZlD240w8ubmU7K0trdVqTgwZFYUtJhp7VDS26Chz5LjNjsVmBasNi91mPtpsYLNisdl3PVqg6P0PqFm3DoDwM88g/vbbsYWEHOzbc9AMw2BT8SbmZ81nUdYiUotTyarIajAo38llc5ESlkLb0La0D2vPgLgB9I/rT3hA+F6vFdi/P+3++1Yj34GIiB9D9HHjxrFq1ap66y699FK6d+/ObbfdtluADuYoo1mzZnHTTTfVrZs5cyYjRozY43UCAgIICGhgUgh/S9rRN2v70rpVR3eO2RGi53P1sZ38VJiIiIiIiIi0Ft6SEvJffInQcWMJGjLE3+U0W1WrVrHt+hvw5OSA3U7ouHFEnnsOQcOGHXLrFYvdTshRRxFy1FH47r2Xirm/U7NhA56CArwFBeZjYQGe/AK8xcXg8+HdsY2NGw/p2rbISLOlyfjxh3SeA5VXmceCrAXMz5zPgqwF5FXl7bZPoD2QlFAzKG8b1rbeY2xQLFZL07W8ERHZF7+F6KGhofTu3bveuuDgYKKjo+vWX3zxxSQnJzN16lQAJk+ezLHHHssTTzzBiSeeyIcffsjixYt55ZVXmrz+Q7ZzctH89VBTDgEhHNM1BoBFWwqpdntxOfw/u7OIiIgceXw+n79LaBX0PoqIvxleL9tv/j8qfv+dkunf0vnHH7EGBvq7rGan5KuvyLr7HozaWpydOtHmuWcJ6NDhsFzL6nQSOnYMoWPHNLjd8HjwFhXhKSzEk5+Pt7DQDNcLCzHcbgyfFzxe89HrxfD6djx6wefF2LnN48WemEDsdddhj4k5LPfyV5XuShbnLK4LzVOLU+ttd9lcDIofxPDE4fSK6UW7sHbEBsYe0kSfIiJNya8Ti+5Leno61r984zty5Ejef/997rrrLu644w66dOnCl19+uVsY3yKEJkBoEpRlQtYKaH8UnWJDSAhzkV1azaLNhRzTNdbfVYqIiMgRxOl0YrVayczMJDY2FqfTqX/cHgTDMKitrSUvLw+r1YqzmU7cJiKtX+6TT1Lx++8AePPyKXz3XWKuvNLPVTUfhsdD7hNPUvjmmwCEjBlD0uOP+aXlyU4Wu93slR4bC926+a2Ovan11rK9fDsZZRmsLVjL/Kz5rMhbgcfnqdvHgoWe0T0ZnjicEUkj6B/XnwBbM+wSICKyn5pViP7rr7/u9TXAWWedxVlnndU0BR1uyQNhXabZF739UVgsFkZ1ieGTJduYm5qvEF1ERESalNVqpUOHDmRlZZGZmenvclq8oKAg2rZtW29QiIhIUyn5djqFr78BQNgJJ1A6YwYFr71O5DnnYAvTHFzekpK6UfoA0ddeQ+wNNxxy65bWoqy2jIyyjLplW9m2uufZFdkYGLsdkxySzIikEQxPHM6whGFEuCKavnARkcOkWYXoR5ykAbDuW8j8S1/0HSH67A153HFCDz8WJyIiIkcip9NJ27Zt8Xg8eL17nvBL9s5ms2G32zWSX0T8ovrPP8m66y4Aoq+8gtibbqJm4wZqNqZS8MYbxP1lnrGmYtTWUpOWRvWfa6let5aatevwlpURcdbfiDzrLCxN+KudmtRUMq67DvfWdCyBgSRNfZiwSZOa7Pr+4Pa6KaktobSmlJLaEkpq/rL85XVmeSYZZRkU1RTt9Xw7+5l3CO/A0IShjEgcQUpYShPdjYhI01OI7k87+6L/z+SiAOuyy8gtqyYu1OWPykREROQIZrFYcDgcOBwOf5ciIiIHyFNYSMb112NUVxM8ahSxN92ExWYjdvJktl1/A4Vvv0PUhRce1j7Z3rIyatato3rtOqrXrqV63TpqUlPB7d5t35wH/03hm28RO/lGwk488bCPBC/7+Wcyb7kVX2UljqQk2rzwPK7u3Q/rNZuSz/CxJGcJ36Z9y58Ff9aF45WeygM+V5QrijahbUgJTalb2oa2pU1oG6Jd0fqiWESOKArR/SlpgPlYtBkqCyEoiuiQAHolhbEms5TfU/M5fUAb/9YoIiIiIiIiLYLhdrP9pn/iyczC0a4tyf95HIvNBkDIuHG4+valeuVK8l9+hYQ772i863q9FH/yCRW/z6N63TrcGRkN7mcNC8PVvTuuHt0J6NEDX1k5+a+8jHvbNjJv/RcFr75G7D9vImT06EYPaA3DoOCll8h7+hkAgoYOJXnaU9ijohr1Ov6SVpzGN2nfMD1tOlkVWQ3uY8FCqDOU8IBwwp3hhAeEExYQtuu5M4zEkERSQlNoE9KGEKf/esOLiDQ3CtH9KTASIjuYIXrmMug8DoBRXWJZk1nKnI0K0UVERERERGT/5Dz2OJWLFmENCiLlueewhYfXbbNYLMT98ybSL72M4g8/JPrvl+BITm6U6xa8/gZ5Tz5Zb509KRFX9x64evQwQ/PuPXAkJ+0WjkeceQaF77xLwWuvUbNhA9uu/QeBAwYQ9383EzR4cKPU56uoIPOOOyn74QcAIi+4gPgpt2Fp4b+4yq/K57vN39WNOt8p1BHKhPYTGJ0ympjAGMKdZlge4gjBZrX5sWIRkZZLIbq/JQ/cEaIvrQvRj+kSw0u/bWLOxnwMw9BPpERERERERGSvij//gqJ33gEg6bFHCejSZbd9gkeMIGjEcCrnLyDv+RdIevihQ75u1arV5D1jju6OuvRSQo49Blf37tgiIvbreGtQEDFXX0XkOWdT8PrrFL79DlXLlrH1wosIPmYUcf/8J64eBz9fWO22bWy77npq1q8Hh4OEe+4m8qyzDvp8/lblqeKX9F/4Ju0b5mfOx2uY85fYLXaOTj6akzqdxOiU0QTYAvxcqYhI66IQ3d+SBsLqz2D7srpVg9pH4nJYySurYX1OGd0TNHO6iIiIiIiINKxq5Uqy77sPgJjrriN0/Pg97ht3001smX8uJV9+SfTllxHQqdNBX9dXWUnmrbeCx0PoxInE/evWgx4EZouIIO7//o/ICy8i/8UXKP7kUypmz2Hz7DmEnXACsZNvxNmu3R6PN2prcefm4s7MxJOdjTsrG3dWJmXf/4C3uBhbTAxtnnmaoIEDD/Z2m5xhGFR6KimoKiCjLIPvNn/HT+k/UeGuqNunT0wfTup4EpM6TCLK1Tpa04iINEcK0f1t5+SimbsmFw2w2xjWIZrfNuQxd2O+QnQRERERERFpkCcvj2033IhRW0vI2LHEXPePve4f2K8fIePHUf7TLPKefoY2zzx90NfOefQxardswR4fT+L99zXKr6gd8XEk3ncf0ZdeSt4zz1I6fTqlM2ZQ+uOPRJx5JsEjRuDOzsKTlWUG5dlmWO7NLwDDaPCcrl69aPPcszgSEw+5vsZQ6a5ke/l2CqoLKKwqNB+rCymo+p/H6gJqvDW7HZ8cksyJHU/kpI4n0SG8gx/uQETkyKMQ3d8S+4HFCmVZUJoFYeZf6qO6xPDbhjxmb8znilEd/VykiIiIiIiINDdGbS3bbpyMJycHZ8eOJD32KBardZ/Hxd54I+Wzfqbsxx+pWrWawD69D/jaZT//TPFHHwGQ9MjU/W7fsr+c7dqR/MR/iL7icnKnTaPit9kUf/RR3TUbYnE6sScm4EhMwpGQgD0xAWe7doRNmoTV5WrU+vaX1+clrSSNVfmrWJm3klX5q0gtTsVn+Pb7HIH2QKJd0QxLHMbJnU5mQNwArJZ9/zmLiEjjUYjub85giO0OuX+ak4vWheixwFoWbS6g2u3F5dDkHyIiIiIiIrJL9kMPU7VsGdbQUNo8/xy2kJD9Os7VtSvhp5xMyVdfkzdtGm1ff+2AruvJyyPrzrsAsw968IgRB1z7/nL16EHbl1+m8o8/yH/tNXwlpTiSErEnJOJITMSRmGA+T0rEFhnp9znF8qvy68LyVXmrWF2wul77lZ3CA8KJccUQFRhFtCuaKFcU0YHm41+fR7uiCXIE+eFORETkrxSiNwdJA3eE6Euh+wkAdI0PIS40gNyyGpZsLeKozjF+LlJERERERESai6IPd4zKtlhI/s/jBHQ4sLYeMTfcQMmM76j4/XcqFi4ieNjQ/TrOMAwy77gTb1ERAd27E/vPmw6i+gMXNGQIbYcMaZJrHYgtJVuYvW02K/NXsipvFZkVmbvtE2gPpFd0L/rE9qFfTD/6xPYhLijOD9WKiMjBUojeHCT1h+XvwvZdfdEtFgtHd4nh86Xbmb0xTyG6iIiIiIiIAFC5dCnZDz0EQOxNNxFy7LEHfA5nmzZEnvU3it7/gLynniLog/f3axR30bvvUTFnDpaAAJIffwyr03nA127JDMNgbeFaZqXPYtbWWWwq2VRvuwULnSI60SemD31j+9Inpg+dIjphtyp+ERFpyfT/4s3BXycXNQzY8cHl2K6xfL50Oz+vzeX243v4sUARERERERFpDtzZ2Wy7cTK43YROmkT0VVce9Lmir7mG4s+/oGr5csp/+ZXQsWP2un/Nxo3kPv44AHG33kpAly4Hfe2WxOvzsix3GbPSZ/Fz+s/1RpvbLXaGJAxhSMIQ+sb2pVd0L0Kc+9dWR0REWg6F6M1BfG+wOqCqCIq2QJT5M7zRXeOwWS1szC1na0EF7aKD/VuniIiIiIiI+I2nsJCMa67Fm59PQNeuJD380CH1AHfExRF10YUUvPoaedOmETL62D1OTOqrrWX7Lbdi1NYSfMwoIi84/6Cv2xLUemtZmLWQWemz+CXjFwqrC+u2BdoDOSrpKMa1G8cxbY4hzBnmx0pFRKQpKERvDuwBkNDbnFg0c2ldiB4e5GBo+yjmpxXw09pcLj/6wHrciYiIiIiISOvgzs4m/bLLqU1LwxYVRZvnn8MadOgTTkZfcQVFH35EzYYNlE6fQfjJJzW4X95T06hZvx5bZCRJDx1aeN8cGYbBtvJtrMhbwexts5m9bXa9CUHDnGGMThnN2LZjGZk0kkB7oB+rFRGRpqYQvblIGmiG6NuXQu8z61aP6xFnhuh/5ihEFxEREREROQLVbtlC+mWX487MxJ6YSNvXX8eZktIo57aFhxN9+eXkTZtG3rPPEjZpIhaHo94+FfPmUfjmmwAkPvQQ9tjYRrm2P1W6K1mdv5qV+StZkbeClXkr6402B4gNjGVs27GMazuOwQmDcVgdezibiIi0dgrRm4vkgbD4dchcXm/1cT3j+ff0tSzaUkhJpZvwIP2lLSIiIiKHx/PPP8/jjz9OdnY2/fr149lnn2Xo0KF73H/atGm8+OKLpKenExMTw9/+9jemTp2Ky+VqwqpFWrfq9etJv/wKvPn5ONu3p+0br+NISmrUa0RddCGF77yDOz2d4s8+J/Lcc+q2eYqKyJxyOwAR55yzz77pzZFhGGwp3cLKvF2B+cbijfgMX7397FY7PaJ6MDh+MOPajaNPTB+slobb24iIyJFFIXpzkbRjctGs5eDzgtUGQLvoYDrHhZCaW86vG3I5tX+y/2oUERERkVbro48+4uabb+all15i2LBhTJs2jYkTJ7J+/Xri4uJ22//9999nypQpvPHGG4wcOZINGzbw97//HYvFwpNPPumHOxBpfaqWLyf9qqvxlZYS0L07bV97FXtMTKNfxxocTMw115Dz0EPkv/AC4aeditXlwjAMsu+9D09uLs4OHYi/7V+Nfu3GZhgGWRVZrC1Yy5+Ff/JnwZ+szFtJaW3pbvvGB8XTL7YffWP70i+2Hz2iexBgC/BD1SIi0twpRG8uYruBIwhqyyF/I8R1r9s0vkc8qbnlzFqrEF1EREREDo8nn3ySK6+8kksvvRSAl156ienTp/PGG28wZcqU3fafN28eRx11FOefb04u2L59e8477zwWLlzYpHWLNCfekhKw2bCFhBzyuSrmzSPj+hswKisJHDCAlJdfwhZ2+CawjDjnbArffBN3ZiZF771P9OWXUfL5F5T9+CPY7SQ9/nij9GBvTD7DR0ZZRl1gvrZgLWsL11JSU7LbvgG2AHpG96RvTF/6xfWjT0wfEoIT/FC1iIi0RArRmwurDRL7Qfp8c3LReiF6HC/9tolf1ufi9vpw2PRzMhERERFpPLW1tSxZsoTbb7+9bp3VamX8+PHMnz+/wWNGjhzJu+++y6JFixg6dChpaWnMmDGDiy66qKnKFvErw+ejdvNmqpYto3L5cqqWLad20yYsDgfhp51K1GWXEdDh4Oa1KvvpJ7b/82YMt5vgo46izbPPHPYA2+p0EnP99WTdcQcFr7xC0PBhZD/0EACxN95IYO9eh/X6+2IYBptLNrOmYA1/FvzJ2sK1rCtcV2/yz53sFjudIzvTPao7PaJ60C+2H10ju+KwqT2qiIgcHIXozUnSQDNE374U+p9ft3pA20iigp0UVtTyx5ZCRnZq/J/viYiIiMiRKz8/H6/XS3x8fL318fHxrFu3rsFjzj//fPLz8zn66KMxDAOPx8M111zDHXfcscfr1NTUUFNTU/e6tHT39goizZWvooKqVaupWr6MymXLqFq+Al/J7iOeDbeb4k8+pfjTzwgdP57oK68gsG/f/b5OyVdfkXnHneD1EjphAkn/eRyr09mYt7JH4aecTMFrr1GblsbWCy7EqK4maPBgoi+/rEmu/1cen4d1hetYkrOEpTlLWZq7lOKa4t32C7AF0DWyKz2ietAj2ly6RHTBaWua90xERI4MCtGbk+QdfdEzl9ZbbbNaGNMtjs+WbuOnP3MVoouIiIiI3/366688/PDDvPDCCwwbNozU1FQmT57Mgw8+yN13393gMVOnTuX+++9v4kpFDo7h81H2449ULvqDyuXLqFm3Hnz1J6K0uFwE9ulD4IABBA7oT2D//tRu3kzBq69R/ssvlM2cSdnMmQQNG0b0FVcQfPRRWCyWPV6z8N33yPn3vwEIP/10Eh98AIu96f7ZbrHbiZ08me2TJ2NUV2MNDSXpsUex2GyH/drVnmpW5a+qC82X5y2nylNVbx+XzUXP6J70jO5pBuZRPegQ3gG7VdGGiIgcXvqbpjlJGmA+Zq8CTy3Yd31zflxPM0SftS6Hu0/qsdcPXiIiIiIiByImJgabzUZOTk699Tk5OSQkNNwz+O677+aiiy7iiiuuAKBPnz5UVFRw1VVXceedd2K17t6C8Pbbb+fmm2+ue11aWkpKSkoj3olI48l5eCpF775bb509KZGg/gPM0Lx/f1zdu2Fx1G8RYo+MJOjFF6jZuJGC116nZPp0KhcupHLhQgJ69CD68ssJmzSxXjhuGAYFL79M3rSnAYi8+CLip0zB0sB/R4db6ITjCOzfn6rly0m4714cSUmH5TqV7koW5yyuC81XF6zG4/PUr8UZysC4gQyMH8ig+EH0jOqpliwiIuIXCtGbk6iO4IqA6mLIXbMrVAdGdYnFabOytaCS1NxyusSH+q1MEREREWldnE4ngwYNYtasWZx22mkA+Hw+Zs2axfXXX9/gMZWVlbsF5bYdo1UNw2jwmICAAAICAhqvcJHDpGr5coreew+AyPPPI2joUAL798exhy+VGhLQpQtJjz5C7OQbKfzvfyn65FNq1q4l85ZbyJs2jahL/07EGWdgcbnIffw/FL7xBgAx119PzHX/8NvAKYvFQsqrr+DJziagS5dGPXelu5LZ22fz45Yfmbt97m4jzeMC4xgYb4bmA+MG0iWyC1aL5gQTERH/U4jenFgsZnCe9gtkLqsXogcH2BnRKZrfNuTx09pchegiIiIi0qhuvvlmLrnkEgYPHszQoUOZNm0aFRUVXHrppQBcfPHFJCcnM3XqVABOPvlknnzySQYMGFDXzuXuu+/m5JNPrgvTRVoiw+0m6977wDAIP/VUEu6555DO50hKIv7224m59loK33+fonfexb1tGzkP/pv851/A1aMHFb//DkD87VOIuuSSRriLQ2MLDcUW2jj/5vxrcD5n2xyqvdV125JDkhmaMNQcaR43iDahbfSraxERaZYUojc3yQPNEH37Uhhcf/KW8T3idoToOVw7upOfChQRERGR1uicc84hLy+Pe+65h+zsbPr378/3339fN9loenp6vZHnd911FxaLhbvuuovt27cTGxvLySefzEMPPeSvWxBpFIX//S8169dji4ggbsptjXZeW0QEsf/4B9GXXkrxZ59T+OabuLdvNwN0q5XEBx8k4swzGu16/lTprmT2ttn8uHX34LxNSBsmtJ/AxPYT6RGlVqUiItIyWIw9/daylSotLSU8PJySkhLCwsL8Xc7u1n4DH10I8b3h2t/rbcosrmLkIz9jscDiO8cTHaKfwoqIiIi0NM3+82gT0nshzU3ttm2knXQyRnU1iQ8/TMQZpx+2axkeD6XffU/p998TceYZhI4de9iu1RR2Buc/bPmBudvn1gvOU0JTmNBuAhPaT1BwLiIizcr+fh7VSPTmJmmg+Zi7FmorwRm0a1NEIL2SwliTWcrP63I5a7AmYRIRERERkZZv59guf4arhmGQff8DGNXVBA0dSvjppx3W61nsdsJPPonwk086rNc5XLw+L+sK1zE/az4LMhewNHcpbp+7bvvO4Hxi+4l0j+qu4FxERFo0hejNTVgShMRDeQ5kr4S2w+ttHtcjnjWZpcxaqxBdRERERERaPnduLlvOPgdnu3a0ff01LHb//DO17LvvqJgzB4vDQcJ99yn0bcD28u3Mz5zP/Mz5LMxeSElNSb3tbUPbMqH9BCa0m6DgXEREWhWF6M2NxWKORt/wndkX/X9C9ON6xPPMrI3M3phHtduLy6FJm0REREREpOXKe+YZPNnZeLKzKXr/A6IuvqjJa/CWlJD9sDlpbvQ1VxPQsUOT19AcldaWsihrkRmcZ80noyyj3vZgRzBDEoYwInEEI5JG0D6svYJzERFplRSiN0fJO0L0zKW7beqdHEZ8WAA5pTUsSCtgdLc4PxQoIiIiIiJy6KrXraPks8/rXuc9/TShEyfg2DGhbVPJffIpvPn5ODt2JPrKK5v02s1NeW05X6R+wfebv2d1wWp8hq9um81io29s37rQvHdMb+xWxQoiItL66W+75mhnX/Ttu4foFouFcT3ieX9hOj+tzVGILiIiIiIiLZJhGOQ88igYBqHHT8KTmUXVihXkTH2ENtOearI6Kpcuo/ijjwBIvP8+rE5nk127Ockoy+D9te/zReoXVLgr6ta3D2vPiKQRjEgcwZCEIYQ4Q/xYpYiIiH8oRG+OkgaYj4WboKoYAiPqbR7fI473F6Yza20uD55q6OdyIiIiIiLS4pT/8iuVCxZgcTqJv+UWvGVlbD7zb5R9/z3ls08n5JhjDnsNRm0t2ffeA0D4mWcQNGTIYb9mc2IYBktylvDu2nf5JeOXulHnHcM7cl738xidMpqE4AQ/VykiIuJ/CtGbo+BoiGgLxemQtRw6jq63eWSnGAIdNrJKqlmTWUrv5HC/lCkiIiIiInIwDLeb3MceAyDq73/HkZyMA4i66CIK33qL7AcepOO332B1uQ5rHQVvvkXNxlRsUVHE33rrYb1Wc+L2uvl+y/e88+c7rC1cW7f+qKSjuKjnRYxMGqnBWiIiIn+hEL25Shpohujbl+4WorscNo7uEsPMP3OYtTZXIbqIiIiIiLQoRR98SO2WLdiio4m+alcP8pjrr6f0++9xb9tG/osvEffPmw5bDbXp6eS/8AIA8VNuwxYRcdiu1VwUVRfxyYZP+HDdh+RV5QEQYAvg5E4nc2GPC+kU0cnPFYqIiDRPVn8XIHuQvKMvegOTiwIc18OcaOentTlNVZGIiIiIiMgh8xYXk/f88wDE3ngjtpBdPbZtIcHE33kHAAVvvEFNauphqcEwDLLvux+jpobgkSMIO/nkw3Kd5iK1KJX759/PcZ8ex7PLniWvKo/YwFhuGHADM/82k3tH3KsAXUREZC80Er25qptcdFmDm8d0j8NigVXbS8guqSYh/PD+zFFERERERKQx5L/4Ir6SEgK6diXib2futj10/HhCxoyh/JdfyL7vftq+83ajtxYp/fZbKubNwxIQQMK997a61iWltaUsylrE/Mz5zM+aT0ZZRt22HlE9uKjnRUxqPwmHzeHHKkVERFoOhejNVVJ/wAKl26A8F0Li6m2ODQ2gf0oEy9KLmbUuhwuGtfNLmSIiIiIiIvurZvNmCt97H4C42/6FxWbbbR+LxULCXXeyacECKhcvpuTLr4g4/bRGq8FbXEzO1EcAiLn2WpztWv6/pdw+N6vyVjE/az7zMuexOn913SShAHaLnWNTjuWinhcxMG5gq/vSQERE5HBTiN5cBYRCTFfIX2/2Re82abddxveIN0P0tbkK0UVEREREpNnLfeIJ8HgIPvYYQo46ao/7OZKTib3uH+T+5wlyH3uMkNHHYo+MbJQacv7zH7yFhTg7dyL6sksb5ZxNzTAMtpZuZV7mPOZnzeeP7D+ocFfU26d9WHtGJo1kRNIIhiQMIdgR7KdqRUREWj6F6M1Z8kAzRM/cc4j++A/rmZuaT2WthyCn/jhFRERERKR5qliwkPKfZoHNRvy//rXP/aMuuYSSr76iZmMqeU8+SeKDDx5yDZV//EHJp58BkHj//ViczkM+Z1MxDIM/C/7k601f80vGL2RVZNXbHhEQwfDE4YxMGsnwxOEkhiT6qVIREZHWR6lrc9ZmCKz4ALbMbXBz1/gQ2kQGsq2oirkb85nQK6GJCxQREREREdk3w+sl59FHAYg85xwCOu17EkuLw0HC/fez9fwLKP7kU8JPO42gQYMOugZfbS1Z994HQMTZZx/SuZpSTkUO36Z9yzebvmFTyaa69Q6rg4FxAxmeNJwRSSPoEdUDq8Xqx0pFRERaL4XozVmnseZjxkKoLgVXWL3NFouF8T3ieWveFn5am6MQXUREREREmqWSr76mZu1arKGhxNxw/X4fFzRwIBFn/Y3iTz4l+7776PD551gcBzcZZsGrr1KbloYtJoa4/7v5oM7RVKo8VcxKn8XXqV+zIGsBBgYAAbYAxrYdy4kdTmRIwhCCHEF+rlREROTIoBC9OYvqAFGdoHATbJ4NPU7abZedIfrP63Lx+QysVk0QIyIiIiIizYevooK8p54CzIk8D7S3eezNN1P20yxqNqZS+N//En3FFQd0fPWGDeQ984zZSgaIv30KtvDwAzpHU/AZPpbkLOHrTV/z45YfqfRU1m0bGDeQUzufynHtjiPUGerHKkVERI5MCtGbu87jYNEmSP2pwRB9aIcoQgPs5JfXsnxbMQPbNs5kOyIiIiIiIo2h4PU38OTl4UhJIfLCCw74eHtkJHH/+hdZt99O3nPPEzrpeJxtkvd5XG16OnnPPUfpN9+CYYDVSuSFFxB2wgkHcxuHTXppOl9v+ppv075le/n2uvVtQtpwSqdTOKnTSaSEpvixQhEREVGI3tx1GgeLXoFNs8wPfpb6I82ddivHdItl+sosZq3NUYguIiIiIiLNhjs7m4I33gAg7pZbsB7kRJ7hp51KyeefU/nHH+T8+9+0efEFLJaGf4Xrzskh/4UXKf7sM/B4AAidOJHYG2/Yr17sTaGouogftvzAt2nfsiJvRd36EEcIE9tP5OROJzMwbuAe71FERESalkL05q790WBzQnE6FKRCTJfddjmuRzzTV2bx05+53Dqxux+KFBERERER2V3eU09hVFcTOHgQoROOO+jzWCwWEu67l7TTTqf8118pnzWL0PHj6+3jKSqi4JVXKXr/fYyaGgCCR40idvJkAnv3OqT7aAzVnmp+3fYr0zdNZ+72uXgMM+C3WqyMSBzBKZ1OYWzbsbjsLj9XKiIiIv9LIXpzFxACbYebPdFTZzUYoo/uFovNamF9ThkZhZWkRGlyGRERERER8a+qVasp+eprAOJvm3LIo6oDOnUi+rLLKHj5ZbL//RBBw0dgCwnGW1ZG4ZtvUfjWW/gqzT7igYMGEXfTZIKGDDnk+zgUPsPH4uzFfJv2LTO3zqTcXV63rUdUD07qeBLHdzie2KBYP1YpIiIi+6IQvSXoPN4M0TfNguHX7LY5IsjJ4HaRLNxcyE9rc7j0qA5+KFJERERERMRkGAY5jz4CQPippxDYp3ejnDfm2msonTEDd0YGeU8+iSM5iYJXXsVbUgJAQM8exP3znwQffbRfW6FsKNrAt2nfMiNtBjmVOXXrE4MTObHjiZzU8SQ6RTSP1jIiIiKybwrRW4JO42DmPbB5DrirwbH7z/vG94hn4eZCZq3NVYguIiIiIiJ+VfbjTKoWL8HichH7z3822nmtLhcJ99xNxpVXUfT++3XrnR07EnvjjYROOA6L1dpo1zsQbp+bzzZ8xqcbPmV90fq69aGOUCa0n8BJHU9iYPxArBb/1CciIiIHTyF6SxDfC0ISoDwb0udDpzG77TK+ZzwPzVjLgrQCSqvdhLkcfihURERERESOdL7aWnL/8x8Aoi+7DEdCQqOeP2TUKMJOOJ7SGd/hSEoi5vrrCT/lZCx2//zz1jAMfsn4hSeXPMnW0q0A2K12jkk+hpM6ncQxbY4hwBbgl9pERESkcfj1K/AXX3yRvn37EhYWRlhYGCNGjOC7777b4/5vvfUWFoul3uJyHQGTrlgs0Hmc+Tz1pwZ36RATTMfYYDw+g9kb8pqwOBERERERkV0K33gDd0YG9thYoi+/7LBcI+mRR2j3ztt0/P47Is443W8B+pqCNVz2w2VM/mUyW0u3EuWKYsrQKfx69q88PfZpjmt3nAJ0ERGRVsCvI9HbtGnDI488QpcuXTAMg//+97+ceuqpLFu2jF69Gp49PSwsjPXrd/00zp997ppU53Gw/D3Y9PMedzmuRzwv56Xx0585nNQ3qQmLExERERERgeIvviRv2tMAxP7zn1iDgw/LdSxOp18nDc2uyOaZpc/wTdo3ADitTi7udTGX976cEGeI3+oSERGRw8OvIfrJJ59c7/VDDz3Eiy++yIIFC/YYolssFhIa+eeALULHMYAFcv+Eku0QnrzbLuN6xPPy7DR+WZ+Hx+vDblOvPRERERERaRql3/9A1p13AhB50UWEn36afws6DCrcFby+6nXe/vNtarw1AJzY8UQmD5hMYkiin6sTERGRw6XZpKxer5cPP/yQiooKRowYscf9ysvLadeuHSkpKZx66qmsWbNmr+etqamhtLS03tIiBUVB8iDz+R5Gow9sG0FUsJOSKjdzNuY3YXEiIiIiInIkK//tN7bfeiv4fISfeQbxt09pVb8a9vg8fLLhE078/EReXfUqNd4aBsYN5IMTP+CRUY8oQBcREWnl/B6ir1q1ipCQEAICArjmmmv44osv6NmzZ4P7duvWjTfeeIOvvvqKd999F5/Px8iRI9m2bdsezz916lTCw8PrlpSUlMN1K4ffzr7om2Y1uNlus3Jqf7ONy0d/ZDRVVSIiIiIicgSrWLiIbTdOBrebsBOOJ/GBB7BY/f5PzUbz+/bfOeubs3hg/gMUVBfQNrQtT41+ircmvUXvmN7+Lk9ERESagMUwDMOfBdTW1pKenk5JSQmffvopr732Gr/99tseg/S/crvd9OjRg/POO48HH3ywwX1qamqoqampe11aWkpKSgolJSWEhYU12n00ifSF8MYEcEXAv9LAatttl3XZpUyaNge71cKCO8YRE6JJbERERESak9LSUsLDw1vm59FGpvei5atasYL0Sy/DV1lJyJgxtHnmaSwOh7/LOmSGYbA0dymvrnyV3zN/ByDMGcY1/a7h3G7n4rC1/HsUERGR/f886tee6ABOp5POnTsDMGjQIP744w+efvppXn755X0e63A4GDBgAKmpqXvcJyAggICAVhIkJw8CVzhUF8P2pZCy+0Q63RPC6NcmnBXbSvhi6XauPKZj09cpIiIiIiKtXvW6daRfeRW+ykqCRgwnedpTLT5Ar3RX8m3at3y4/kM2Fm0EwG61c17387i679WEB4T7uUIRERHxB7+H6P/L5/PVGzm+N16vl1WrVnHCCScc5qqaCZsdOo6GP7+C1J8aDNEBzh6SwoptJXy0OIMrRnVoVb0IRURERETE/2rSNpN+2eX4SksJHDCAlOeew9qCBy+llaTx0bqP+HrT15S7ywFw2Vyc2PFELut9GW3D2vq5QhEREfEnv4bot99+O8cffzxt27alrKyM999/n19//ZUffvgBgIsvvpjk5GSmTp0KwAMPPMDw4cPp3LkzxcXFPP7442zdupUrrrjCn7fRtDqPN0P0TbNgzO0N7nJyvyQe/PZPUnPLWZpezKB2kU1cpIiIiIiItFa127aRfumleAsLCejZg5SXX8IaHOzvsg6Yx+fht4zf+GD9ByzMWli3vl1YO87pdg6ndDpFI89FREQE8HOInpuby8UXX0xWVhbh4eH07duXH374geOOOw6A9PR0rH+ZkKaoqIgrr7yS7OxsIiMjGTRoEPPmzduv/umtRqcdk4tuXwKVhRAUtdsuYS4HJ/RJ5POl2/n4jwyF6CIiIiIi0ijcObmkX3oZnpwcnJ060fa117C1sH72+VX5fL7xcz7Z8AnZFdkAWC1WjmlzDOd1O4/hScOxWlrPxKgiIiJy6Pw+sWhTaxWTFz0/HPLWwt/ehN5nNLjLwrQCznllAcFOG4vuHE9wQLPr3CMiIiJyRGoVn0cbid6LlsVTWMjWiy6mdtMmHCkptHv3XRzxcf4ua7+tyFvBe2vfY+bWmXh8HgAiAyI5o8sZnN3tbJJCkvxcoYiIiDS1FjOxqByEzuPMEH3TrD2G6EM7RNEhJpjN+RVMX5nF2UNSmrhIERERERFpLbylpaRfcQW1mzZhT0ig7ZtvtpgAfVnuMl5c/iLzs+bXresb25dzu53LxPYTcdqcfqxOREREWgKF6C1R53Ew/zlInQWGAQ1MHGqxWDhrcBse+349Hy3OUIguIiIiIiIHxVdRQcbV11Dz51ps0dG0feMNnG2S/V3WPi3NWcqLK15kQdYCAOwWOyd2PJHze5xPz+gjqCWoiIiIHDKF6C1R25FgD4SyLMhdC/ENfwD828A2PPHjBpZsLSI1t4zOcaFNXKiIiIiIiLRkhsdDxvXXU7VsGdawMNq+/hoBHTv4u6y9WpqzlBdWvFA3WajdYufUzqdyZd8rSQ5p/uG/iIiIND8K0VsihwvaHwWpP5nLHkL0uDAXY7rF8tPaXD5evI07TujRxIWKiIiIiEhLVjFvHpXzF2AJCqLtq6/g6t7d3yXt0ZKcJby4/EUWZu8Kz0/rchpX9LlC4bmIiEhTKs8DRyAEhPi7kkajEL2l6jzeDNA3zYKjbtzjbmcPTuGntbl8vnQbt07shsOmWeZFRERERGT/VC1fDkDYxIkE9uvn32L2YHH2Yl5a8dKu8Nxq5/TOp3NFnys0WaiIiEhTqimDH+6ApW+D3WXmlz1Pg26TIKBld8hQiN5SdRpnPm6dB7UV4AxucLcx3eOICQkgv7yGWWtzmdQ7oQmLFBERERGRlqxq+QoAAvv19XMlu1ucvZgXV7zIouxFgMJzERERv9o6D764Boq3mq891bDuW3OxBZhzPO4M1F3hfi31YChEb6liukB4WyhJhy2/Q9cJDe7msFk5c1AyL/+WxseLMxSii4iIiIjIfjF8PqpWrQJoVqPQ00rSePyPx5m7fS5ghudndD6DK/pcQWJIop+rExEROcK4q+GXh2Des4AB4W3xnPI8tuBILH9+BWu+hIKNsH6Gudic0GnsjkD9eAiM8G/9+0khektlsUDnsbDkLbOtyx5CdDBburz8Wxq/rs8lu6SahHBX09UpIiIiIiItUu3mzfjKyrAEBhLQpYu/y6G0tpQXl7/Ih+s+xGN4FJ6LiIgcZjUeL9uLqsguraa0ykNplZvSajclVW5Kq9yEFP3Jedsfoo17CwBfW8dyf+GFFLxahsVSRrBzCCHO4fQK2s5xxnxGuX8n2ZMOG76HDd/jtdjJiBzGtsQJFLQ5jsE9OpIcEejfm94DhegtWefxZoi+adZed+sUG8KQ9pH8saWIz5Zu47oxnZumPhERERERabGqVqwEILBXLyx2//3T0evz8tnGz3hu2XMU1RQBMLrNaG4Zcgvtwtr5rS4REZGWzjAM8strySiqJKOwkvSCStILzSWjsJKs0moMY/fjbHi52vYNk+2f4bR4yTPCuN19JT/5Bv3l3FBe46G8xkM2McziZOBkOlu2caJ1ISfYFtLNuo32hb/TvvB33KsfYF3hv0k+8R9N9wYcAIXoLVmHY8Big4JUKNoCke33uOvZg1P4Y0sRHy/O4NpjO2G1WpqsTBERERERaXmqVpj90F1+7Ie+KGsRj/7xKBuKNgDQMbwj/xryL45KPspvNYmIiLQUXp9BXlkNmSVVZBVXk1VSxfbiKjIKq8zQvLCSKrd3r+cIctpIDHcRHuggPNBBJ1sOF+c8StuK1QBsix/H+qEPcll4PDft2CfUZcftNajYEaKX13j+8rw3FTXjmV7tYVbxJjrn/0TP4l9oU5tGQJv+TfCuHByF6C2ZKxxShkH6PEidBUMu3+OuJ/ZN5P5v/mRrQSULNxcyolN0ExYqIiIiIiItTdXKHSPR/dAPfVvZNp5Y/AQ/pf8EQJgzjH/0/wdndzsbh9XR5PWIiIg0R8WVtWQUVu0IyavIKqkms6S67nlOaTUeXwNDyf/CYoGk8EBSogJpGxVE26ggUnYsbaOCiA52YrFYzKHli1+HH+8GdyUEhMHxj9Gm37m0sTQ8WDc2NGAfd9ANOMF8WrCJrlEdD/xNaCIK0Vu6zmPNEH3Tz3sN0YOcdk7ul8gHizL4eHGGQnQREREREdkjX2UlNevXA00bole6K3lt1Wv8d81/qfXVYrPYOKvrWVzX/zoiXBFNVoeIiPiJzwcbf4DYbtCMA9WmVO32sqWggs15FaTlV7D5L0thRe0+j7dZLSSEuUgMd5EYEUhSuIs2OwLytlFBJEW4CLDb9n6S0kz46vpdLaU7HAOnvgARKY1whztEd2q8cx0GCtFbus7j4ed/Q9pv4HWDbc+jMs4enMIHizKYsSqL+07pRXigRnCIiIiIiMjuqlavBp8Pe0ICjvj4w349n+Hj27RvmbZkGnlVeQAMSxzGbUNuo0uk/yc1FRGRJlBdAl9cC+ungysCrvgJYo6MvwMqajxklVSxraiqXkielldBZklVg33Jd4oNDSAp3EVieCCJES6Sdj5GBJIUHkhsaAC2Q2nr/OfX8PUNUF0MdheMvx+GXgVW68GfswVSiN7SJfSDoBiozIeMRdB+z70B+6dE0DU+hA055Xy9IpOLhmsSHhERERER2V31zlYufQ9/P/TUolTunXcvK/PNa6aEpnDL4FsYkzLG/Pm4iIi0fjlr4KMLoTDNfF1dDO+dBVfMguCW3U3B7fWRXVJttlopNluvZBabPcq372i7UlLl3us5Ql12OsaG0DEmmI4xwXSIDaZDTDDto4MJDjiM8e7CV+C7W83nSQPg9Fcgtuvhu14zphC9pbNaodMYWPUJpP601xDdYrFw9uAU/j19LR//kaEQXUREREREGrRzUtHD2crF4/Pw1pq3eGH5C7h9boLsQVzd72ou7HEhTpvzsF1XRESamVWfmiOd3ZUQngInPQXTb4aizfDRBXDxV2DfV2/tw6O4spaf1+Xyw5pslmwtwmeA1WJmbFYLWC0WrBYLlrrn1L22WS2UVnnIKave60jynUJddpLCA2kfE0SHmBA6xu4IzGOCidrZl/x/7c+JD4ZhwG+Pwq9TzddDroRJU/faAaO1U4jeGnQeb4bom2bB+Hv3uusZA9vw6PfrWLW9hD8zS+mZFNZERYqIiIiISEtgGAZVy3eG6IdnJHpqUSp3/X4XawrWAHBMm2O4Z/g9xAcf/tYxIiLSTHjd8ONdsPAl83XHMXDm6+bI8/M/gdcnQPp8+Oo6OONVcwbMJpBdUs2Pf2bzw5psFqQV4t3HxJz7w2mzkhhh9iXf2WYlKcJsu5IcEUhiuItQ134E1IYBBamQ9itsng1b5pitb056EjqNPeQ6AbMv/fe3waJXzNej74Bj/9Vk739zpRC9Ndj5H0nWCijPg5DYPe4aFezkuJ7xzFiVzceLM7jvlF5NVKSIiIiIiLQEnuxsPHl5YLPh6tW4/17439Hnoc5QpgydwskdT1brFhGRI0lZNnx8CWQsMF+PugXG3AHWHRNcxnWHs/8L7/3NHDga3RlGTzls5aTmlvPDmmx+XJPNim0l9bZ1TwhlQq8ERneLJTTAjtcw8PnAZxgYhvloLuYX0b6d63wGwQF2EiNcxAQHYD3YvuTFGWZgvnk2bP4NyrLqb68qgndOh5E3wNh7wH4Iv+byuuHLa833HOD4x2HYVQd/vlZEIXprEBIHCX0heyVs+hn6nbPX3c8enMKMVdl8sWw7U47vjsuxjxl4RURERETkiFG1wuxNHtCtK9bAwEY7b2pRKnf/fjerC1YD5ujze0fcS1xQXKNdQ0REWoCt8+CTv0N5DgSEwekvQ/cTdt+v0xg48Qn4ZrLZViSqI/Q9m2q3l4055azLLmV9dhnrssvILKki1OUgItBBZJCDiCAn4X99HrRzm5OIIAehLgertpfw4xpzxPmmvIq6y1osMLBtJBN7xTOhZwLtY4Kb7r0BqMjfFZhvnr2rT/xOtgBIGQodj4V2R5uB9+LXYd6zsHmOOZo/pvOBX7e2Ej65BDb+CFY7nPYS9D2rce6pFVCI3lp0HrcjRJ+1zxB9VJdYksJdZJZU8+OfOZzSL6mJihQRERERkeausfuha/S5iIgAZiuSBS+aLVwML8T1hHPehehODe7u8xlsa3823m4r6bD+ddxf/INbvsvjm+L2NEKHlXocNgsjO8UwsVcC43vGERfqOrQTGgakzjKzOm8teGrMx78+r/dYA55ac1R5/vr657JYIWmgGZp3OAZShoHjL19ytxthdqn4+nrIWg4vHwMnPAb9L9j/FixVxfDBuWb7HHsgnP02dJ1waO9BK6MQvbXoPB7mPmX+B+rzmROO7oHNauFvg9rwzM+pfPxHhkJ0ERERERGpU7XSHIneGCF6Q6PP1ftcRI5IXg/YWngMV1MGS98GnxdiukJMF4hot3/3VVMO39wIqz8zX/f+G5zyDDh3jfIuq3Yzb1MBczbmsXp7KRtyyqis9WJhDC84VnK87Q/urXyY5TxAaVAK3RPC6J4YSveEUFKigqio8VJcWUtxpZviqh2P//u8spaKWi8AwU4bo7vFMaFXPGO6xxG2Pz3J90dhGsy4FVJ/OvhzxPXaFZq3Gwmu8L3v3+MkSBoAX1xt9kn/6jozIzzpKQiM2PuxZTnw7hmQsxoCwuH8j8xgXupp4f/1Sp02Q8EZApX55rdcSf33uvtZg1N45udU5qbmk1FYSUpUUNPUKSIiIiIizZbhdlO92gy8A/sefIi+2+hzRyi3Db2NUzqdotHnInLkWfIWfPtPs3VJdCezLUndsuN1UFTznrixOB0+OM8MWv/K6jDvKaYLRHfZEa53NduJ7Ax+81Phowshb63ZJmTCQzDsanwG/Lm9hN825PHbhjyWbi3C8z9DzJ02K13iw/g17t8M2n4zcWVrmJXwPLYrf8ISFHVQt1Lr8VFS5SYs0E6AvRFbHHtqYN4zMPs/4KkGmxN6nAIBoWAPMF/bA8x2LHan+fp/1zmCILH/Xuc73KPwZLj4K/h9Gvz8EKz5HLYthjNfg7bDGj6maAu8fRoUbYbgOLjoC0joffDvQSumEL21sDuhw7Gwfrr5Tdc+QvSUqCCO6hzN76kFfLJkGzcf17Vp6hQRERERkWaresMGjJoarOHhONu3O6hzbC7ZzB1z7tDocxERgPQFMP3/wPBBdTFsX2Iu/ysgHKI61A/ZkwdDbDPIazL+gA/Pg4o8M2htfzQUbDTDcU8V5K0zl/8VEm8G69kroaYUQuIpOelVfqnqzOyPVzB7Yx755bX1DukQE8yxXWMZ1C6SHomhtI8Oxm7b0W2h7At4bRz2ok3w8cVw4ecHNYmm024lNjTgYN6JPds8B6bfDPkbzNcdR8MJTxxcb/JDYbXBqP8zM8LPLjdD8jePh2Nvg2Nu2TVxK0DOGnjnDCjPNn9RcPGX5v/upEEK0VuTzmPNEH3Tz+Z/GPtw9uAUfk8t4NPFGUwe1wXbwc4SLCIiIiIirUJdP/Q+fbDspUXknqzIW8F1s66jpKZEo89FRMqyzbDX54Gep8Ext5qtPv53Kd0ONSVmP+us5fXP0fccGHcPhLfxww0AKz8xW4N4ayC+D5z/4a5afD4o3WYGx/mpOx43QP5GM5gtzzEXICO0P3c5/o/Z/y3HMJbXnT7YaWNEpxiO7RbLsV1iaRu9l04JofFmq5HXJ5otS76ZDKe94N8R/OV5Zo/3lR+ar4PjYNJU6H2mf+tqMxiungMzboGVH8GvD0Par3DGKxCRAukL4f2zoLrE7E1/4ecQlui/elsAheitSadx5mPGQijOMP+j2IuJvRIID3SQWVLN3NR8ju16ED8VERERERGRVqN6xcH3Q5+7fS43/3ozVZ4q+sT04anRT2n0uYgcuTy1ZoBengOxPeDU5yEgpOFWGe4qc8RwYRoUbDIf8zfC1rlmAPrnVzD8H3D0P8EV1jT1+3zw61SY/Zj5utuJcMYrZFRYyd5SSFFFLUWVtRRVuimqSKaoMpbCisEUV9VSaKnFbS0hunorHcjEi5UZecPwYI6C7pkYxjFdY+tGnDvtB/ClbXwvOOsteP9sWPG+OXJ/PwaSNjqfD5a9DTPvNX9hgAWGXA5j7953D/Km4gozQ/NO48xR8unz4KWjYNi18PvT5q8I2gyFCz6GwEh/V9vsKURvTaI6QPIg82dB758Nl3631/9wXQ4bp/VP4r/zt/L+wq0K0UVEREREjnB1I9H79T2g46anTeeuuXfhMTwclXQUT45+kiCH5l0SkRagYBOs+BAGXdK4o71/uMMc5BgQDue+Zwboe+IIhLge5vJXmcvgh7vMMH3uk+aknmNuh4F/P7yTlNZWwlf/gDVfAJDT52reCvo7Pzy3mLS8iv08iZ0MOrGcTkQHOzm+cwzHdo3lmC4xxIW5Dq2+LuPhhMfMNjk/P2jmYb3PPLRzHojs1WaP+22LzNcJfeCkp6HNoKar4UD0OwdShsBnV5iZ4W+PmOs7j4ez3643uavsmcUwDGPfu7UepaWlhIeHU1JSQlhYE31715SKM+C18ebPZtqP2md/qA05ZUx4ajYWC/x6y2jaRes/HBEREZHDqdV/Hj0Aei+aF29xMRuGjwCgy/x52CP3b1Tae2vf45FF5j/Ij+9wPA8d9RAOm+Ow1Ski0mjKsuHVsWY7ldAkuOjz3YPsg7H8ffjyWvP5eR9Bt0kHfy7DgPXfwcy7oSDVXBfTDSY8CF0mNH7LkLJsfB+chzVzKR6LnYesV/NmxVF1mx02C8kRgUQEOYkKdhIR5CAqyElksJPIICeRQY5dz4MdRAQ6D2yk+YH4/g5Y8Lw5Keffv4WUoYfnOjvVlJsB9PwXwPCCMwTG3gVDrjy8X2o0Fq8bfnnYnPy099/glGcPqqd8a7O/n0cVordGWSvNSQNqy83eWae/vNf/U/37m4v4dX0el4xox/2nagZeERERkcPpiPg8up/0XjQv5XPmkHHlVTjbtaPTD9/vc3/DMHhu+XO8svIVAM7rfh5Thk7BajlMYYmISGNyV8GbJ0Dm0l3rXBFw/sfQdtjBnzdzObwxETzVcOwUc+R4Y/C6YclbZouVygJzXYdjYMJDkHhgvx5qSFm1m2WLfqPv7KuJ8ORTaIRwTe0/WWT0ICTAzpjucUzoGc+x3WIJczWTL0p9XvjoQlg/w2xH0nEMBMdAcKz5GPSX58Ex5p/vnvIx346JXysLoCLffKzM/8vzAtgy1/zCBaDHKXD8oxCW1FR323g8NWBv5IlVW7D9/TzaAr4mkQOW2BfO/i+8d7bZOyu8jTkJxR5cOaojv67P4+PF2/jncV2JCNK3UCIiIiIiR5qqnf3Q+++7H7rX5+WhhQ/xyYZPALi+//Vc1fcqTSAqIi2Dz2eOFM9caoavF34G300x23O8farZc/tgRo9XFMBHF5kBetdJcOxtjVezzQFDr4S+Z8OcJ2HBi7B5Nrx8DPQ7zxwRHZ7c4KGGYVDj8VHt9lLl9lLt9lFV66Xa42VdVhk//plN8KbveNz2PEGWGjb6krnVeQc9+/fjHz3jGdEpmgC7rfHupbFYbXDma+ZA0qwVsObzfexv3xGsx0BQNBi+HSF5PlQWmqPL9yWiLZzwBHSd0Dj34A8K0A+KQvTWqvN4OOUZcwblOU9AeAoMvrTBXUd2iqZnYhh/ZpXy3sJ0rhvTuYmLFRERERERf9vZD93Vd+8jGmu9tUyZM4WZW2diwcJdw+/i7G5nN0WJIiKN47dHzH7fVgec8645v9zFX8Enl8DGH+HD881WFwMu2P9zej3w2WVQkg6RHcyuANYD/2XOxpwyvludzfers8korMRqtWDbuVh2Ph9NckBPrqh9j3Ge32DF+9Ss+Iwvg05nnm0Imd4IsjyhlHssVO8IzffM4FrbN9zm+BCAtPBhVJz8Kp93bIvV2gK+GHUGw6Xfw4bvzUlcK/J2LAXm487R5DWl4POY7Y/Ls/d8voAwCIqqH7YHRZvPQ5Og+4ng1JwfRyK1c2ntfplq/uVgscF5H+7xm7Ivl23npo+WExMSwO9TxjTPbxhFREREWoEj7vPoXui9aD4Mw2DD8BH4Skpo/+mnBPbu1eB+5bXl3PTLTSzMXojD6uCRUY8woX0LHo0nIkeeVZ/CZ5ebz095DgZetGub1w1f3wArPjBfH/cAHDV5/8478174fRo4guCKWRDfc78OMwyDP7NK+X51NjNWZbFpvyfuNPW1bOJOx3sMs66rt95nWCggjBwjkhwjklwjglwiybdEUmSNpsQRQ5U9kuv4kDHVs8yDhl4FE6e2jP7eB8pdvaNVy1+Cdau9fkgeFK1R2kcgtXMR0+gpUJIBy9+DT/4Ol06HpAG77XZi30Qe/X4dWSXVfLUsk7OHpDR9rSIiIiIi4he1W7bgKynBEhCAq1vXBvcpqCrg2p+uZW3hWoLsQTw99mmGJw5v4kpF5LDJWmm2qgiM8Hclh0/GH/DlP8znI2+oH6CD2TLltBfNPtrznoGZ90B5Lhz34N5Hla/50gzQAU59bp8BumEYrNhWwners/huVTbphZV12xw2C6O6xDKpdwKD2kViGOD1GbsWw6j/2jeMat+5rMz4kZT1bxFYsQ1nVR5WPMRSQqylhN5s2b0Iz44FzIGXxz9qtotprRwus93NHlreiOyLQvTWzmKBk5+G0kxI+8Xsk37FTIhsX283h83KpUe15+EZ63h1ThpnDW6jfoYiIiIiIkeIulYuvXphcew+Ydz28u1cPfNqtpZuJTIgkhfHv0ivmIZHq4tIC7ToVZhxizkSd9Kj0Odve56AsaUqzjDbtHhroNsJMP7+hvezWGDCg2aQPvNumP+cOWr51OfMkP1/5a7bFcyPuB56n9ngaX0+gyXpRXy3KpvvV2eRWVJdty3AbmV0t1iO753I2B5xBzdxZ7cLYfyFOy9mjrouy4Ky7F2P5dn/8zrHvM/TX4JOYw/8miJHEIXoRwKbA85+25x1OmcVvPs3uPxHs8fTX5w7tC3PzEplY245v27IY0y3OD8VLCIiIiIiTal65Y5JRRvoh76haAPXzLyGvKo8koKTePm4l2kf3r6JKxSRw2b7Evj+dvN5ZQF8fgWs/AhOetIcmd4a1JTBB+dCRS7E94EzXjUnpdybo240A+avroOVH5rvzdn/NXtw71RdAh9dAO4KaD+K0lF3kZldSlZxNduLq8gqqSKzuJrM4io25ZWTX15bd2iQ08bY7nEc3zuR0d1iCQ5oxIjOaoWQWHNJ3Ms8F74dE2nu670QEYXoRwxXGFzwMbw2Hgo2mt++XvSl+XOWHcJcDs4dksJrczfz2pw0hegiIiIiIkeIquXmSPTA/v3qrV+dv5qrZl5FWW0ZnSM689L4l4gPjvdHiSJyOFQVwcd/B58bup8ESf3ht8cgdSY8PxzG3W32yfZXyOqpMXtYhyUf/Mh4nxc+uxJyVkNwHJz3AQSE7N+x/c8zByB+fIn5nrx9KmVnvsfv2w02ZpcwbuXN9CxNJdcSw5mbLyHjgZ/3erpQl53jesQzqXcCx3SNxeXwc3it8FxkvylEP5KEJcEFn8IbkyB9Pnx5DZz5Rr2+Xpce3YE3523h99QCVm8voXdyuB8LFhERERGRw81XVUX1hg0ABPbbFaKvylvF1TOvpsxdRr/Yfjw/7nnCA/TvA5FWw+eDL66FknSz5etpL4ArHHqcCt9MhvR58P0UcyLOU57d74kyG4XXDcvehdmPQ+l2SOgLw/9htkqxOw/sXD/dCxu+A1uAGaBHHNgccL7OE9g06V1Svr8U17Y/yJ42hvtrpnCmbTY9HXOpMRxcXnMTGUYQABFBDhLDA0mOcJEYHkhSRCBJES6SIwLp2yYCp30vvdVFpNmyGIZh+LuIprS/M662aptnwztnmN80j7wBJvy73uYbP1jG1ysyOa1/EtPO3X0SUhERERE5ePo8uovei+ahcskStl5wIfbYWDrP/g2LxcKqvFVcNfMqyt3lDIwbyAvjXyDYEbzvk4lIy/H70+bEmbYAs+VrUv9d23w+WPoWzLwXakrBaoejb4ZR/1fvF+2Nzuc1Q/tfp0LR5t23h8Sbk18OugyCo/d9vqVvw9c3mM/PfN3s9b4fckurmb0xn9kb8pizMY+iSjddLNt42/kIiZZCCi0RRBglWDFY1PcBqnufXxeWBzk1XlWkJdnfz6P6L/tI1OEY8xvmz6+Eec9CeFsYdlXd5itHdeTrFZl8uzKLf03qTlJEoB+LFRERERGRw6lqxY5+6P37NRigvzj+RYIcQX6uUkQa1db58NOOiTWPf6R+gA7mL9YHXwZdJ8H0W2D9dJj9GPz5JZz8DLQb0bj1GAas/QZ+eRjy1prrgmLM0L7XabDiA1j4ijkx5s//htn/gX7nmqPTY7s1fM4tc+Hbf5rPj52y1wC9xuNlyZYiftuYx+wN+azNKq23PSTATsfOg1iQ8gEnrbiOqKJUc8Pgyxh60uRDvHkRaQkUoh+p+p4Nxenw84Pw3b8gsh10nQhAnzbhjOgYzfy0At6at4U7Tujh52JFRERERORwqVph9kN39e3LyryVXD3zagXoIq1ZRT58eikYXuhzFgy6dM/7hiXBue/B2q9hxq2QvwHenASDL4fx95rtXw6FYUDqLDObyFpurnOFw8gbYdg1u3qXj/o/GHEDrPkCFjwPWStgyVvm0nk8DP8H7vajmZtawDcrM6FwE/fnTCbU8DAn4BgeW3U0npVz8Pp8eHwGXp+Bx2vgMww8PoPSKjc1Hl+90vq2CeeYLrEc0zWWAW0jcNh2tGEZ8iN8cyPYXTDpkUO7fxFpMdTO5UhmGGafs6X/haQBcNWvdZt+XpfDZW8tJjTAzrzbxxLqcvivThEREZFWRJ9Hd9F70TxsHDMWT1YW7mfu4er8Zyh3lzMofhAvjHtBAbpIa+Pzwnt/g00/Q0xXuPKX/Z9ks6oIfrwblr1jvg5NNNvDpgyF0CSwHeA4zS2/m+F5+nzztSMYhl9rtp0NjNjzcYYBW+fBghcw1k3HghlrpZLCq+5J/OwdwAfOf9PZmslyXyfOqb2bGvbdRz0mJIBjusZwbNdYju4cQ3RIwIHdj4i0SGrnIvtmscDYu8weYZnLoDTT/JYZGN01js5xIaTmlvPRHxlcMaqjn4sVEREREZHG5s7JxZOVhWG1cEPmNMotlQrQRQ5EcTosfhM6jIKOY8x/Zzdnc54wA3R7IJz99v4H6ACBkXDqc+Yv27+ZDIVp8Nnl5jaLDcKTIaIdRLSF8BTzcecSlrwrZN++xGzJsuln87UtAIZcAUf/E0Ji912HxUJG2AC+jL2PhRknMa7kC862/UpnSwaPOl7F57BixUdVYCKF497iuaB47FYLNqtl16PNgs1qrXvtcthoFxWE1drM//xExG8Uoh/pQuKgzRDYtgjWzzD/4gKsVgtXHN2BKZ+v4o25m7lkZPtdP10SERERkVbp+eef5/HHHyc7O5t+/frx7LPPMnTo0D3uX1xczJ133snnn39OYWEh7dq1Y9q0aZxwwglNWLUciqqVZiuXjFgLhZZKBscP5vlxzytAF9kfeRvg7VOhLBPmPmmO7B56ldmrOyDU39XtLu1Xs+c4wElPQtxBtm7tcAxcO8/sS77mcyjOAJ/b/EKhOL3hYyw2CEvGHRCBI9echwGrHQZcBMfcagbw+1BcWcv0VVl8sXQ7i7cW7VgbxmLHpazrcj1Xhsyl0+b3sJZsA0cwgZd8wtiEPgd3jyIi/0MhukD3E8wQfd2uEB3gtAHJ/OfH9WSWVDNjVRan9t/3X2oiIiIi0jJ99NFH3Hzzzbz00ksMGzaMadOmMXHiRNavX09cXNxu+9fW1nLccccRFxfHp59+SnJyMlu3biUiIqLpi5eDtnXeTOzA+kSDIQlDeG7scwrQRfZH1kp453SozDdHXVcVmf3CZ9xiTtjZ/3wzUI/p7O9KTaVZ8NkVgGEG1/3PP7TzOQJh3N3m4vNBec6uEL1461+ep2OUZGDx1kJJOg7S8RoWvvQdzUves6hY3YbIzWlEBW8jIshJVJCDyGAnkUFOIoOdRAU5Ka1289Xy7fyyLo9ar9m33GKBozrFcNqAZCb2it/RgvYY8P4L0n6B8DYH/yWBiEgD1BNdzG/Pnx8CVgf8Kw1cu96XZ2dt5ImZG+idHMY31x+Npbn/NE1ERESkmWuun0eHDRvGkCFDeO655wDw+XykpKRwww03MGXKlN32f+mll3j88cdZt24dDsfBzZ/TXN+LI8Xy3OVsvuhCum/18v15nbj6jo8VoIvsj4w/4L0zoboEEvrCRV+CzQErPoBFr0BB6q59O42DYVdD5+PA6qdfd3s98PYpsPV3iO8NV/xkhuCHUWm1m+9XZ/PNikzmpeYSbZTQxpJHkrWQ9UY7NvoSD+q8PRLDOH1AEqf0SyYh3NXIVYvIkUg90WX/xXaF6M7mX/SpM6H3mXWbLhzejud/TWX19lIWpBUyolO0HwsVERERkcOhtraWJUuWcPvtt9ets1qtjB8/nvnz5zd4zNdff82IESO47rrr+Oqrr4iNjeX888/ntttuw2azNVXpR7zqDRtwtmuHNeDAJsBbnruca3+4ihcyvQBcdu6jCtBF9sfmOfD+OeCugJRhcP7HuybBHHY1DLnSHAm96BXY8ANsmmUuke3NbQMu3PukmYfDLw+ZAbozBM7672EL0KvdXmatzeXrFfVHjYOVxJQOnNjvKE7qm0hcaABlNR6KK9wUVtZSVFFLUWUthTseiyrdFFXseu0zYFyPOE4fkEz3BH3hKiL+oRBdTN1OgHnPmC1d/hKiRwY7OWtQCu8s2Mqrc9IUoouIiIi0Qvn5+Xi9XuLj4+utj4+PZ926dQ0ek5aWxs8//8wFF1zAjBkzSE1N5R//+Adut5t77723wWNqamqoqampe11aWtp4N3EEKp8zh4wrryKwXz/avfM2Fqdzv45blruMa2ZeQ2xWJS43WEJCCOuitgci+7ThR/j4IvBUm33Bz/1g94k5rVboPM5cCtPgj9dh2TtQtAV+vNMMtPueA/0vgITeh31EOBt+NPu1A5zyTKO3l3F7fcxNzefr5Zn8uCabilpv3bYucSGc0i+Jk/sl0T4muN5xYS4HYS4HbaP15Z2ItAwK0cXU/UQzRN84E7xu86doO1x+dAfeXbiVn9flkppbRue4ZjhBioiIiIg0KZ/PR1xcHK+88go2m41Bgwaxfft2Hn/88T2G6FOnTuX+++9v4kpbr6L33gegasUKcv7zHxLuuGOfxyzPXc41M6+h0lPJhPL2wCaC+vbB4q82EyItxZovzZ7iPjd0PR7Oegsc+2gnEtURJj4EY+6AlR+bo9Nz/4Qlb5qLxQrRXSC+lxmox/cxn4clmU2/D1VxBnxxlfl8yJX1BswdCMMwyCuvYVtRFduLqthebD5uK6pkxbYSCitq6/ZNjgjklP5JnNIvie4JoWoJKyKthkJ0MbUZAsGxUJEHW+ZCpzF1m9rHBDOhZzw/rMnhtTmbeeTMvn4sVEREREQaW0xMDDabjZycnHrrc3JySEhIaPCYxMREHA5HvdYtPXr0IDs7m9raWpwNjIq+/fbbufnmm+tel5aWkpKS0kh3cWRx5+RSPnt23euit98haOAgwiZN3OMx28q2cf3P11PpqWRY4jBOXxtFOZtw9evXFCWLtFzL34evrgPDB73OgDNeqTfwbJ+cwTD4Uhj0d7OtyqJXYfNsqCqE/PXmsubzXfsHRpq9y+N77wjXe0Fsj32H9n/lqYVP/m5OeJo0wAzz/4fPZ1BW46Gs2k1plYeSKjdZJX8JyourzOC8uIpaj2/3a+wQE+LkxD6JnNI/iYFtIxWci0irpBBdTFYbdJ1k/sxs/Yx6ITrAVcd05Ic1OXy+dDv/N6EbsaEH1nNRRERERJovp9PJoEGDmDVrFqeddhpgjjSfNWsW119/fYPHHHXUUbz//vv4fD6sO0Yxb9iwgcTExAYDdICAgAACDrB3tzSs5OuvwOcjcNAgggb0p+C118m6805cPbrjbNdut/0r3ZVM/mUyJTUl9I7uzbNjnyXz6bMACOyrQTIie7ToVZhxi/l8wIVw8jPmv58PhsUC7Y82F8OAsmzIWQ3ZqyBnjfk8f6MZfG+ZYy67Dga7C6x28/o2x47nO15bd702rDYqK0oJLk2jyhbKE0G3seW9VZRWuymtclNW7aG02k15jQfD2L/SrRZICHORHBlIckQgyZGBtIkMokNMMIPbRWK36dcsItK6+TVEf/HFF3nxxRfZsmULAL169eKee+7h+OOP3+Mxn3zyCXfffTdbtmyhS5cuPProo5xwwglNVHEr1/1EM0RfNwOOf6zez8cGtYtiQNsIlqUX8878Ldw8oZsfCxURERGRxnbzzTdzySWXMHjwYIYOHcq0adOoqKjg0ksvBeDiiy8mOTmZqVOnAnDttdfy3HPPMXnyZG644QY2btzIww8/zI033ujP2zgiGIZByWfmqNWIM84g/JSTqVy2nKolS9g2+Sbaf/gBVper3v73zLuHDUUbiHJF8dSYp3BWuqndtAmAQI1EF2nY3Gnw0472VMOugYlTzZ7njcFigbBEc+ly3K717mrIW2cG6jlrdgTsq81g3VO1f6cGdnYgv7HqSmau9gE5e9w/wG4lLNBBqMtuBuV/CcmTIwJpExlIQrgLh4JyETmC+TVEb9OmDY888ghdunTBMAz++9//cuqpp7Js2TJ69eq12/7z5s3jvPPOY+rUqZx00km8//77nHbaaSxdupTevXv74Q5amY6jwREEpdsgeyUk1v8wfdWojlz73lLeXrCVa0d3JtB5kN++i4iIiEizc84555CXl8c999xDdnY2/fv35/vvv6+bbDQ9Pb1uxDlASkoKP/zwA//85z/p27cvycnJTJ48mdtuu81ft3DEqFq2jNotW7AEBRE2aSIWh4PkJ59g8+lnULNuHTkPPUTigw/W7f/mmjf5YcsP2C12nhr9FAnBCZT//jsAjrZtsUdF+etWRJonw4BfHobZj5mvR/0fjL27cfqU74vDBUn9zeWv9VTkg7sSfB7weXc8usHnIauwnO9XZTBnfQ5udy12vIQ6oXOnrnSL68dgl52wQHMiz9C65/a64DzArn/bi4jsi8Uw9vfHO00jKiqKxx9/nMsvv3y3beeccw4VFRV8++23deuGDx9O//79eemll/br/KWlpYSHh1NSUkJYWFij1d1qfHgBrPsWjp0CY26vt8nrMxjzn19JL6zkwVN7cdGI9v6pUURERKQF0+fRXfReHJzMO++k5LPPCT/zDJIe2tXnuGLePNIvvwIMg8RHphJx2mn8vv13rv3pWgwM7hp2F+d0PweAvBdeIP+ZZwk76SSS//O4v25FpPkxDPjhDljwgvl63L0w6ua9H+MHhmEwNzWft37fws/rc+vasnSKDebvR3XgzIHJBDnVwVdEZF/29/Nos/ktjtfr5cMPP6SiooIRI0Y0uM/8+fMZP358vXUTJ05k/vz5TVHikaHbjtY466bvtslmtXD50R0AeH3uZry+ZvX9i4iIiIhIq+erqKD0u+8BiDjzzHrbgkeOJOa66wDIvv8Btiybza2zb8XA4MwuZ3J2t7Pr9q1esRJQK5cjhqeG/W5+fSTzuuHrG3YF6Mc/3uwC9MpaD+8t3MqEp2Zz0euLmLXODNDHdIvl7cuG8tPNx3LR8HYK0EVEGpnf/1911apVjBgxgurqakJCQvjiiy/o2bNng/tmZ2fX/Zx0p/j4eLKzs/d4/pqaGmpqaupel5aWNk7hrVXXSWCxQs4qKNoKkfUnJTprcBuenLmBLQWVfLw4g/OGtvVToSIiIiIiR57S77/HqKzE2b49gQMG7LY95tprqFq6lIp589h64w3UXuylb3I/7hh2B5YdrSgMw6BqxQoAAvtpUtFWL30hvH2KOSnmiU/4u5rmq7IQPrkENs82/018yrPme9YM/D979x0eRdn1cfy7u+mdkEYIJdQk9N6bgIpYqAoWwF5Q7K9i76gPdmzYUJSiIIqAKL13CC0UqQmQAoT0nt33j4FgJEAISTYkv8917bWzM/fMfZaHB3fPnjm3zWbjyKlMflx7mGkbYkjOzAXA3cnC0La1GNm5LqF+7he5ioiIXA67J9EbN25MZGQkycnJzJgxg5EjR7Js2bLzJtIv1bhx43j11VdL5VpVgnt1qNURolfDnj+h4wOFDrs5OTCmd0NenxPFuHm76BMeiL+ns52CFRERERGpWpJOLyjqPXhQQVL830wWCzXefYet/fsQcDybRxa4cO137+NkcSoYkxsTQ35SEiYnJ1zCwsotdrGDM61J8rJg47fQ+RGoVtfeUVU8x/fAlFvg1EFwdIfBX0FY/zKdMi/fSmJGDifTcjiRll3wfCIth5Np2ZxML7w/O89acG6d6m6M7FSXIW1D8HJxLNM4RUTEYPckupOTEw0aNACgTZs2bNiwgY8++ogvv/zynLFBQUHExxdeUTo+Pp6goKDzXn/s2LE88cTZ269SUlKoVatWKUVfSYX1P51En3tOEh1gZKc6zNpyhB1HU3hjbhQfDTu3AkZEREREREpX9oGDZG7eDBYL3jfddN5xk47N4q8b8njlJ2i/LQunP5bAsGEFx89UobuEh2NycjrfZaQy2DMPjm40tm1WWPMpXKce+IX8swBm3AXZKeBdG4ZPhaCmZTLVybRsZm05yszNR9kdl3LJHXa6NKjOnZ1D6RUWgMVcDouciohIAbsn0f/LarUWar/yb506dWLRokU89thjBfsWLFhw3h7qAM7Ozjg7q1L6koRdB38/D4dWQeYpcK1W6LCDxcy4gc256dOV/B55jMGtQ+jeyN9OwYqIiIiIVA3Js4wqdI9u3XAMCChyzPIjy/lkyyfYapk4Puoagr77i/g338KlaTNcmzYBIPNMP/SW6odeqVnzYdHrxnbdbnBoBWyeDD2eAXc/+8ZWEdhssGYCLHjJ+IGhdme4ZXKp/9nk5ltZsjuBGZuOsHh3Ann/WlvMZILq7k5Ud3emuocTfh5nn/08jP1+ns5Udzf2uTpZSjU2EREpPrsm0ceOHUu/fv2oXbs2qampTJkyhaVLl/LXX38BMGLECGrWrMm4ceMAePTRR+nRowfvvfce/fv3Z9q0aWzcuJGJEyfa821UPr71wD8cju8yfpVvfvM5Q5qFeDOqcyjfrjrIC7/t4O/Hu+PiqP+gi4iIiIiUBVteHkm//QYYrVyKcij5EM8sfwYbNm5udDM9R7zAkcMPk7Z4MUcfe4zQX2di8fI6W4neXP3QK7Xtvxjf6Vx8jOTwDwMgNhLWT4Rez9k5ODvLy4Y5j0PkT8br1iPguvfAofTuzNgdl8IvG4/w25ajnEzPKdjfopYPQ9qEcHVEIH4ezqooFxG5QpjtOXlCQgIjRoygcePG9O7dmw0bNvDXX3/Rt29fAKKjo4mNjS0Y37lzZ6ZMmcLEiRNp0aIFM2bM4LfffqNp07K51apKC7vOeN4997xDnri6ETW8XYhOzODjRf9c+HrZaVoNXkRERESkhNJWrCD/+Aksvr549uhx7vGcNB5d8ihpuWm0CmjFs+2fxWQyETzuLRxr1iT3yBGOPfcc1qwssnbvBsC1RctyfhdSbvJyYMmbxnbXx4y7i7s+ZrxePxFy0u0VWWH2+I6YlgDf32Ak0E1muPYduOHjUkmgJ2Xk8P3qQ9zwyUqu/XAF36w8yMn0HPw8nLmvez3+frw7v4/uwh0d6xDo5aIEuojIFcSulejffPPNBY8vXbr0nH1Dhw5l6NChZRSRFGjcH1a8B/sWGr/SO5zbEsfD2YFXb2zCfZM3MXH5AW5qWZPGQZ7nXuvwGvhxEDQdDDdNKIfgRUREREQql+RfTy8oeuON5/Qxt9qsPLfyOQ4kHyDANYD3e76Po8VYbNDi7U3NDz/k8K23krZwEbHPPQe5uViqV8exZnC5vw8pJ5smQVI0eARB+/uNfeE3QrVQY/HMzZOLXP/qsqTGGXNmnoLMJMhKMp4zT51/2+II7e+Fro+Di3fpxlOU2G0wdTikHAFnbxj6HTToXeLL2Ww2TqTlsO1IEr9uPsqCqHhy8o0FQB0tJnqHBTK0bQg9GvnjYLFrDaOIiFymCtcTXSqI4FbGB660ODi4Ahr2KXLY1U2CuKZJIH/tjOe5Wdv55f5OmP/9a3peNvwxBnIzYM+fRqWBSb+2i4iIiJSGunXrctdddzFq1Chq165t73CkjOSdPEnqkqUAeA8aeM7xL7d9yZKYJTiaHfmw14f4uRbu6ezarCkBY58l/rXXSZn3p7GvRQtM+lxeOWWnwfLTi4f2eBqc3IxtswU6PwJznzB6gbe720hil4Yjm+Dba8Cae2nn5WfDyg9g8w/Qcyy0GVV6Mf1X1GyYdb/x3bR6Axg+DfwaXjxEq43Y5EyiT2Zw6GQGhxPTOXwig8OJGRw+mU5GTn6h8RE1vBjaNoSbWtbE110L94qIVBZKokvRzGZo3A82fQe755w3iQ7wyo1NWPnPCTYdPsW0DTHc2uFfX+BWfQQn9hrbGScgLR48g8o4eBEREZGq4bHHHmPSpEm89tpr9OrVi7vvvpuBAwfi7HzuXYRy5Uqe/Qfk5eHSvDkujRoVOrYkegmfRX4GwIsdX6SZf7Mir1Ft+HAyN24iZd48AFzVD73yWvc5pCcYVeetRxY+1vJWWDoOkmNg56wi17+6ZPl58MejRgLd3R+8go0+7K7VwNXn9Pbp12e2zxyP3wELXoaT/8C8p2DdF9DnVQjrX3rFVzab8aPCmfY29XoZFeiu1f4zzMb+4+lsOJTI3vhUDp80kuQxiZkF1eVFMZugZjXXgqrzJsHlUFEvIiLlzmSzVa1G1SkpKXh7e5OcnIyXl5e9w6nY/lkAPw0xKtKf2GUk1s/ju1UHefWPKLxcHFj4ZA8CPF3gxD74vLNRXWBxgvwcuH0mNDh/Ql5ERESksiuLz6ObN29m0qRJTJ06lfz8fG699VbuuusuWrduXSrXLyv6bH5xNpuNAzfcQM6+/QS9+irVbjmb9IxJiWHonKGk56YzPGw4z3W48GKR+WnpHLr5ZnIOHKDuLz/j2qzohLtcwTIS4aOWkJ0Mg76G5kW0Ql3+P1j8BgQ2hQdWXn6yevUn8PcLRlL64Y3g7nfxc/4tPxc2fw9LxhmFVwC1O8HVb0BI28uLLTUe5j9j/GAA0OFB47oWB/LyreyKTWX9oUTWHzzJxkOnCi0A+m+OFhO1qrlRp7obdaq7U6e6G3Wru1O7uhsh1VxxdrBcXpwiImI3xf08qiS6nF9eNrxbD3LS4J7FENLmvEPzrTYGfLqK7UeTuaFFMJ8Ma2ks1nJoBdTvDS5exgeXPq+eXdBGREREpAoqy8+jubm5fPbZZzzzzDPk5ubSrFkzxowZw5133lkhW3fos/nFZW7bxqGbb8Hk4kLDFcuxeJ5dg+jRxY+yOGYxrQNa8/U1X+NovngbjPy0NHJjYnAJDy/LsMVeFrxk3A0c2BTuX1F0IVTmKfigqfE977aZF7zr+KKSouHTDkaLlBs/gdYjSn6trBQj9jWfQl6msa/pYOj9ElSrW7xrZCQa30EProCDy+HEHmO/2ZHca//HZr8b2XAokXUHE9l8+BTp/2nF4uxgpmUtH5qHeFPXz506vkbCPNjHVYuAiohUUsX9PKp2LnJ+Ds5G1XjUb7Bn7gWT6BaziXGDmnHjhJX8sfUYD1VbT/ihFeDgCte/D9tnGEn0+B3lF7+IiIhIFZGbm8usWbP47rvvWLBgAR07duTuu+/myJEjPPfccyxcuJApU6bYO0wpgaSZxoKiXtdcXSiBviFuA4tjFmM2mXmx44vFSqADWDw8sCiBXjmlHIN1XxrbvV86/53ErtWM3uNrJsCqD0ueRLfZYN7TRgK9dmdoeXvJrnOGixf0fhHa3mW0XomcAjtmwq4/oP190P2pc1qwkJUMh9cYCfNDyyFuB3C2TtCGiXj3xnzmfBfTfg8iJ39todM9XRxoW6ca7UJ9aV/Xl2Yh3qoqFxGRIimJLhcW1t9Iou+eZ3wQu4CmNb25q0soM1dupcba142dPZ8xqgaCTt8qGr+zTMMVERERqUo2b97Md999x9SpUzGbzYwYMYIPPviAsLCwgjEDBw6kXbt2doxSSsqamUnK3LkAeA8afHa/zcr4jeMBGNJwCA2qNbBLfFLBLHsX8rKgVkdoePWFx3Z80Og/fmiFsSjoBQqmzmvXH7B3Ppgd4foPLtj+85J414QBnxkx/v0CHFhqJPy3/Ag9/g/8w05Xmy+HY1vAVrhf+WFLHVbkhrEiL4K11nCSszxOH7Hi5+FMh1Bf2tU1EudhQV6qMBcRkWJREl0urGFfMFng+C5IPAC+9S44/PG+jWi1+Tl8rKkcd62Pf6eHjQOBTY3nE3uNNjEOWuxKRERE5HK1a9eOvn378vnnnzNgwAAcHc+tRg4NDWXYsGF2iE4uV+rff2NNS8OxVi3c2p3tDT33wFyiTkbh7ujOQy0fsmOEUmGc3A9bJhvbfV6+eJ9z7xBodjNsnWJUo98y+dLmy0qBP58xtrs8CgFhFx5fEkHN4I7fYN8iWPAiJETBX+f2/T9oDWSNtQlrrBGstUZwHB8AXBzNNAr2JCzIk7Z1fGkX6kvd6m4VsrWViIhUfEqiy4W5VoO6XYxf+XfPg84PX3C4+7E19LcuwWoz8VDKCF4/nklYkOPZFdqzkuD4bqjRolzCFxEREanMDhw4QJ06dS44xt3dne+++66cIpLSdKaVi8+ggZhOV/lm5WXx8ZaPAbin2T1Ud61ut/ikAlnyFljzoEFfqNO5eOd0GWMk0Xf9ASf2gd8l3NGw5E1IPQbVQo02K2XFZMLWoDfL85vyz19f0OfkFBxM+ay1RrA6P4I11ibEUp061d0IC/JkeJAX4UGeNA7ypE51d1WZi4hIqVESXS6ucX8jib7nIkn0vGyY8xgAK7xvYENCQ8b+up2ZD3TGbDYZlQSHVhh96pREFxEREblsCQkJxMXF0aFDh0L7161bh8VioW3btuc5Uyq6nOhoMtavB5MJ7wEDCvZPjppMXHocNdxrcHv4ZfaglsohdhvsmGFsX6QFZyEB4dDoWqMly5pP4IaPinfe0c2wfqKx3f89cHS9tHiLKSs3n9mRx/h65QH2xqcB7XjL1I62dXyJCPaiXZAndwR50ijQE3dnpTZERKRslVLTMqnUwq4znqPXQPrJ849b+QGc3AcegYTdNh4PZwe2RCcxZX20cfxMSxf1RRcREREpFaNHjyYmJuac/UePHmX06NF2iEhKS9KsWQC4d+mCY40aAJzIPMHX278GYEzrMbg4uNgtPqlAFp9ej6rpYKjR/NLO7fKY8Rw5FVLjLz4+P88onLJZoekQaND70uYrhpNp2Xy86B+6vrOY/5u5jb3xabg7WbirSyjLnu7Fzw904pUbmzC8fW1a1a6mBLqIiJQLJdHl4nxqQ2Az44PS3vlFjzm+F1a8Z2xf+zaBgYE8fU1jAN6Zv5uElCwIOpNE314OQYuIiIhUflFRUbRu3fqc/a1atSIqKsoOEUlpsOXnkzzrNwB8Bg8q2P9p5Kdk5GXQtHpTrgu9zk7RSYVyeDX887exjlWv5y/9/NodIaQ95GfDus8vPn7DVxC7FVy84Zq3Ln2+C9iXkMbYX7fT+e3FvL9gLyfScqjh7cJz14Wx5rnevHRDBLV83Up1ThERkeJSEl2K50w1+p555x6z2WDO45CfY6wC32QgALd3rEOLEG9Ss/J4dU4UBDYxxsftMM4RERERkcvi7OxMfPy51aOxsbE4OKg680qVvnoNeXFxWLy98ehtVPr+c+offv3H6JH+dLunMZv0Va7Ks9lg4avGdusRUL3+pV/DZIKujxnbG741Fgw9n+SjsPgNY7vPK+AZeOnz/YfNZmP1vhPcNWkDfd5fxtT10WTnWWlW05uPhrVk+f/14r7u9fFyOXfRZBERkfKkT15SPGH9jef9iyE3s/CxyClweCU4uMJ14wtWgreYTbw1qBkWs4m522JZdsrPqJDITITU2HJ+AyIiIiKVz9VXX83YsWNJTk4u2JeUlMRzzz1H37597RiZXI6kX2cC4HXDDZidnAB4b9N7WG1W+tTuQ+vAc+8+kCron78hZi04uECP/yv5dRr1A79GkJ0Mmyadf9yf/wc5aUbleutRJZoq32rj4Il0/twey/sL9tL/45Xc+vU6Fu9OwGSCvhGB/Hx/J2Y/3IWbWtbE0aKUhYiIVAwqT5HiCWoO3rUgOQYOLIXG/Yz96Sfh7xeM7V5joVqdQqc1Cfbmri51+WrFQcbO3ssK3wZYTu4x+qJ7BZfvexARERGpZMaPH0/37t2pU6cOrVq1AiAyMpLAwEAmT55s5+ikJPJOnSJt4SLgbCuXVUdXseroKhzMDjze5nF7hicVhdUKi073Qm9/3+V9tzKbofMYmP0wrP0MOjwADk6Fx+yeB7vngNkBbvjQOOciTqXnsDsuld1xKeyONZ73xKeSlWstNM7V0cLQtiHc2SWUUD/3kr8PERGRMqQkuhSPyWQkztdPhN1zzybR/37eqCwPbAYdHyry1Mf7NmJBVDyHTmaw1TmE1uyBuO3QUNVRIiIiIpejZs2abNu2jZ9++omtW7fi6urKnXfeyfDhw3F0VPuDK1HKnLnYcnNxjgjHJTycfGs+4zeOB2B42HBqe9W2c4RSIez81VhrytkLupbCDyvNb4Ylbxp3DG//GVrdfvZYdhrMe9rY7vTw2Tadp+VbbRw4nkZUbApRsUbCfE9cKnEpWUVO5eJopnGgJ42DPGla05sbWwTj4+ZU5FgREZGKQkl0Kb7G1xlJ9L3zwZoPh1bC1qmAyahGsBT9Rc3NyYH3bm7J0C9Ws+CkP60dgfgd5Rm5iIiISKXl7u7OfffdZ+8wpJQk/Wr0PfcZNBiAWftmsS9pH15OXtzf/H57hiYVRX7u2d7kXcaAm+/lX9PBGTo+CAteglUfQYtbz1abLx0HKUfApzZZXZ5i75Ekdh5LYcfRZHYeS2F3XMo51eVn1PJ1JSzIi/AgT8JqeBEW5Emd6u5YzKbLj1lERKQcKYkuxVe3Kzh7Q/pxI4E+53TFQ7t7IKTtBU9tU6caD/asz85lRuVM3rHt+ssnIiIiUkqioqKIjo4mJyen0P4bb7zRThFJSWRFRZG9axcmR0e8r+9Pem46E7ZMAOCBFg/g7ext5wilQtj8A5w6CO7+0OHB0rtumzth+XtwYi/snU9K3b4c2r6Gpms/xwy8kHsnU99YQb7Vds6pro4Wwmt4EhHsZSTNa3jSKNATTy0IKiIilYTymFJ8FkejBcuOGfDrvZAWDx5B0PvFYp3+aO9G3L2rCSSBOXEfttxMTI6uZRuziIiISCV24MABBg4cyPbt2zGZTNhsRnLLdHqh9/z8fHuGJ5coaYaxoKhn3z5YfHz4dssnnMw6SW3P2gxrPMzO0UmFsH+xUS0O0P1pcPYovWu7eGFtexfmVR+we+brXJeaz69OL2E25zMnvwM/nmwM2Kjm5kiTYG+aBHsREexFk2BvQv1UXS4iIpVbiZLoMTExmEwmQkJCAFi/fj1TpkwhIiJCt5JWdmHXGUn0tHjj9XXvgkvxKmKcHMy8OOwqEj/3xNeUyp9LltLv6n5lGKyIiIhI5fboo48SGhrKokWLCA0NZf369Zw8eZInn3yS8ePH2zs8uQSpS5dy6uefAfAeNJi49Dh+2PkDAI+3eRzH87ROlCpk63T4/SGw5kFoD6NyvJSkZOXy84YY/tjUlJ9tDoTlRvE/xy9oaT5Ahsmd2I4v81VoA5oEe1HD26XghzoREZGq4uJLahfh1ltvZcmSJQDExcXRt29f1q9fz/PPP89rr71WqgFKBdOgL5hPf4BvdC2EX9otwo2CvMj0DQdg1cqlHD6ZXtoRioiIiFQZa9as4bXXXsPPzw+z2YzZbKZr166MGzeOMWPG2Ds8Kab0tes4OuZRyMvD6/rrce/SmU+2fEJWfhatA1rTu3Zve4co9mSzwaqPYdZ9RgK96RC4bQY4XP5inIdOpPPK7J10emsRb8zdxdYkV+aYegAw2LISALd+r3Jv/y70jQgk2MdVCXQREamSSpRE37FjB+3btwfg559/pmnTpqxevZqffvqJSZMmlWZ8UtG4eEHHByAgAq4bDyX4ABXcyOifXt96kCd/3lpkTz0RERERubj8/Hw8PT0B8PPz49ixYwDUqVOHPXv22DM0KabMyEhiHnoIW04OHr17EzzuLaISo5i9fzYA/9fu/5S0rMqsVvjreVhwuoVmx9Ew6KvLSqDbbDZW7z/BPd9vpNd7S5m0+hDpOfk0DPBg3KBm9L9/HHD671zNNtD2rst/HyIiIle4ErVzyc3NxdnZGYCFCxcWLFgUFhZGbGxs6UUnFdPVbxiPEjLVaAZAE8sRXj18ii+X7+ehng1KKzoRERGRKqNp06Zs3bqV0NBQOnTowLvvvouTkxMTJ06kXr169g5PLiJr926i77sfW0YG7p07UfP998DBgfEbjFY8/ev1p4lfEztHKXaTlw2/PQg7jF75XP0GdH6kxJfLys1n9tZjfLvyILvjUgv292rsz11dQ+nawO/sDzZt74Kds+CGj8FsuZx3ISIiUimUKInepEkTvvjiC/r378+CBQt4/fXXATh27BjVq1cv1QClEgpsCkBLpxjItvHBgr30aORPk+Di9VYXEREREcMLL7xAerrRHu+1117j+uuvp1u3blSvXp3p06fbOTq5kOwDB4m++x6sKSm4tmpFyIQJmJ2dWRy9mI3xG3G2OPNoq0ftHabYS1YKTL8NDi432mkO+Aya31yiS8WnZPHTumh+WnuYk+k5ALg6WhjSJoRRXepS37+IxUmvfx/6v1eiO49FREQqoxIl0d955x0GDhzI//73P0aOHEmLFi0AmD17dkGbF5Hz8m8MZgecclO4pZGZ6XttPDF9K78/3AUXR1U5iIiIiBTXNddcU7DdoEEDdu/eTWJiItWqVVMLkAos58hRou+6i/yTJ3GOCKfWl19gdnMj15rLB5s+AGBExAhqeNSwc6RiF6lx8OMQiN8OTh5wy2Sof9UlXcJo2XKSH9ce5u+o+IIWmsHeLozsXJdh7Wrj7XaRxWr1b4iIiEiBEiXRe/bsyYkTJ0hJSaFatWoF+++77z7c3NxKLTippBycwa8RJETxfJt8Fh1zYU98Ku8v2Mtz14XbOzoRERGRK0Jubi6urq5ERkbStGnTgv2+vr52jEouJjchgei77iIvLg6n+vWp/fXXWLy8APh5z88cSjmEr4svdze7286RyiXLSoY/HoWkaGjQBxpdCzVagvkSliI78Q/8OMi4hru/sYBocMtin56ckcuMzUf4ae1hDpxIL9jfvq4vIzrX4domQThYSrQ0moiISJVWoiR6ZmYmNputIIF++PBhZs2aRXh4eKFqGJHzCmwKCVF4Je/h7UF3cM8PG/lqxQGuCgugYz21BBIRERG5GEdHR2rXrk1+fr69Q5Fiyjt1ipi77yY3OhrHkBBqf/sNDqd/9MjOz2bitokAjG45GndHd3uGKpcq8QBMGQYnTi/oe3QTLHsHPIKg0TXQuB+E9gCnCxSdHdkIPw2FzETwrQe3/wq+ocWafvuRZCavPcTsrcfIyrUC4OHswMBWNbm9Yx0aB3le7jsUERGp0kqURL/pppsYNGgQDzzwAElJSXTo0AFHR0dOnDjB+++/z4MPPljacUplE9QUtv8M8Tvo0z2QW9rWYvrGGJ78eSvzH+uGp8tFbi0UEREREZ5//nmee+45Jk+erAr0Ci4/LY2Ye+4l+599OAQEUHvSdzgGBhYc//PgnyRmJRLkHsSghoPsGKlcssOrYdptRvLbMxi6PAqHV8L+JZAWB5u/Nx4OLlCvp5FUb3QteAWfvcbev+DnkZCXCcGt4dafwcP/gtNm5uTzx7Zj/LT2MFuPJBfsDwvy5PaOdRjQqiYeziX6yi8iIiL/UaL/om7evJkPPjB69c2YMYPAwEC2bNnCzJkzeemll5REl4sLbGI8x+0A4MUbIlh94AQxiZm8PieKd4e0sGNwIiIiIleGCRMmsG/fPoKDg6lTpw7u7oWrlzdv3mynyOTfrJmZxDzwAFk7d2KpVo3a332LU0hIwXGbzcaUXVMAGNZ4GA5mJT6vGFt+Mlq4WHMhuBUMmwpeNaDjA5CXDYdWwJ75sHc+JMcYz3vnA49DjRbQqB84ucPCV8CWb7SBGfo9OBex2CeQl28lKjaF2ZHH+GXTEZIzcwFwspjp1yyIOzrWoU0drYkgIiJS2kr06SwjIwNPT+N2sL///ptBgwZhNpvp2LEjhw8fLtUApZIKbGY8J+6HnAw8nN14b2hLbpm4hp83HqFPeCBXNwmyb4wiIiIiFdyAAQPsHYJchDUnhyMPP0Lmxk2YPT2p/c3XONevX2jM5oTN7ErchYvFhcENB9spUrkkVissegVWfWS8jhgAAz4v3K7FwdlIijfoA9f9DxKiYM+fRhL9yEaI3Wo8zmhxK9z4MVjO3pWbmpXLlugkNh4+xcZDiUTGJJGRc7aFU0g1V27tUJub29bCz8O5bN+ziIhIFVaiJHqDBg347bffGDhwIH/99RePP/44AAkJCXidXhRH5II8A42FctKPw/FdULMN7UN9ua9bPb5cfoCxv26ndZ1q+iAoIiIicgEvv/yyvUOQC7Dl5XHsySdJX7UKk5sbtSZ+iUtExDnjftr1EwD96/XHx8WnnKOUS5adBr/eB3vmGq+7/x/0HHvhBURNJuNu3MAm0P0pSDsO//xlJNSj10KbO6HXcxxLzmLj4eNsPJTIxkOn2B2XgtVW+FKeLg50rFed4e1r0aNRABazqs5FRETKWomS6C+99BK33norjz/+OFdddRWdOnUCjKr0Vq1alWqAUokFNoUDS4yWLjXbAPDE1Y1Ytvc4u+NSGfvrdibe0Ua3IoqIiIjIFcdmtRL7/POkLliIycmJWp9OwK2I70qxabEsil4EwG3ht5V3mHKpko8YC4jGbweLM9w0AZrffOnX8fCHVreTEn4LsyOPsf5gIpveWcLRpMxzhtbydaVtHV/a1KlGu7q+NAzwwKzEuYiISLkqURJ9yJAhdO3aldjYWFq0ONu7unfv3gwcOLDUgpNKLrCJkUSP31Gwy9nBwvs3t+SmT1eyICqeb1Ye5J5u9ewYpIiIiEjFZTabL1hwkJ+ff95jUraSf/ud5N9ng8VCzQ8/xP104dF/Td0zFavNSoegDjSs1rCco5RLcmQTTBsOafHGXbXDpkCt9iW6VEJKFt+sOshPa6NJy84r2G8xm4io4VWQMG9btxqBXi6l9Q5ERESkhEq8Yk1QUBBBQUEcOXIEgJCQENq3L9kHCKmigk73RY/bUWh3RLAXY/uF89qcKN6ct4s61d3pGxFohwBFREREKrZZs2YVep2bm8uWLVv4/vvvefXVV+0UlQCkLlgAgN/99+N5Va8ix2TkZjBz70xAVegV3o6Z8NtDkJcFAU3g1mngU/uSL3PwRDoTl+9n5qaj5ORbAWgU6EH/ZsG0rVuNlrV8cHfWwrIiIiIVTYn+62y1WnnjjTd47733SEtLA8DT05Mnn3yS559/HvOFesGJnBHY1HiO3wk2m9En8LQ7u9Rl3/E0pqyLZszULfzyQCea1vS2U6AiIiIiFdNNN910zr4hQ4bQpEkTpk+fzt13322HqMSanU362rUAeF7d97zj5h6cS0pOCiEeIXQP6V5e4cmlsNlg2TuwdJzxutG1MPhrcPa8pMtsP5LMF8v2M29HLLbTPc7b1qnGgz3r06txgNqziIiIVHAlSqI///zzfPPNN7z99tt06dIFgJUrV/LKK6+QlZXFm2++WapBSiXl1wjMjpCdDMkxhSo5TCYTr97YhJjEDFb8c4K7v9/Ab6O7UMPb1Y4Bi4iIiFwZOnbsyH333WfvMKqsjPUbsGVm4hAYiHPjxkWOsdlsTNk1BYDhYcOxmC3lGaIUR24W/D4adswwXnd6GPq+BsX838pms7Fq30m+WLaflftOFOzvHRbAAz3r066ub1lELSIiImWgREn077//nq+//pobb7yxYF/z5s2pWbMmDz30kJLoUjwOTuDf2OiJHrfjnNshHS1mPr2tNUM+X83e+DTunrSRXx7opNsbRURERC4gMzOTjz/+mJo1a9o7lCorbflyADy6dz9vz/q1sWvZl7QPNwc3BjbUulIVjtUKM++G3XPA7AD934c2I4t1ar7Vxl874/h86X62H00GjF7nN7UI5v4e9WkcdGlV7CIiImJ/JcpGJiYmEhYWds7+sLAwEhMTLzsoqUICmxpJ9PgdEHbdOYe9XBz5ZmQ7Bn62iqjYFMZM3cLEEW2x6HZHEREREapVq1YoSWuz2UhNTcXNzY0ff/zRjpFVbWnLlwHg0eP8LVrOVKHf1OAmPJ2UVK1wFr9mJNAtTnDrz1C/6L72/2a12pi5+QifLd3PwRPpALg4mhnWrjb3dAslpJpbWUctIiIiZaRESfQWLVowYcIEPv7440L7J0yYQPPmzUslMKkigprCNowk+nnU8nXjqxFtGTZxLYt2J/DG3ChevqFJ+cUoIiIiUkF98MEHhZLoZrMZf39/OnToQLVq1ewYWdWVffAguYejwdERt46dihwTkxLDsiNGon142PDyDE+KI3IKrPzA2L5xQrES6BsPJfLqH1EFlec+bo6M6FSXUZ3r4uvuVJbRioiISDkoURL93XffpX///ixcuJBOnYwPhmvWrCEmJoZ58+aVaoBSyZ1ZXDTu/El0gFa1q/H+zS0ZPWUz3606RKifOyM61S37+EREREQqsFGjRtk7BPmP9NOtXNzbtcXi4V7kmCm7p2DDRteaXQn1Di3P8CqmtOPw9VXgWx9un1nsnuNl4vBqmD3G2O72FLS45YLDY5MzGTdvN7O3HgPA09mBh69qwO0d66gNpYiISCViLslJPXr0YO/evQwcOJCkpCSSkpIYNGgQO3fuZPLkyaUdo1RmZ5LoiQcgJ/2CQ/s3r8HT1xgLM70yeydLdieUdXQiIiIiFdp3333HL7/8cs7+X375he+//94OEUnastNJ9O5Ft3JJz01n1r5ZANwefnu5xVWhrXwfkqLhwBLYNMl+cSQegGm3gTUXIm6CXs+fd2hWbj4fL/qHq8YvY/bWY5hMMLx9LZY83ZP7e9RXAl1ERKSSKVESHSA4OJg333yTmTNnMnPmTN544w1OnTrFN998U5rxSWXn4Q8egYAN4qMuOvyhnvUZ2iYEqw0enrKZXbEpZR+jiIiISAU1btw4/Pz8ztkfEBDAW2+9ZYeIqjZrejoZGzYA4NG9R5Fjftv3G+m56dT1qkun4KLbvVQpSTGw4euzrxe9Buknyj+OzCSYMgwyEyG4FQz4Asznfl222WzM3RZL7/eW8f6CvWTm5tOubjX+eLgr4wY1x8/DufxjFxERkTJX4iS6SKk5U41+gb7oZ5hMJt4c2IxO9aqTnpPP3ZM2kJCSVcYBioiIiFRM0dHRhIae2w6kTp06REdH2yGiqi197Vpsubk41qqFU2jdc45bbVam7p4KwG3ht2E26esYy9+F/Byo0wWCmkFWEix8uXxjyM+DGXfCiT3gGQzDpoLTuYuARh1LYdjEtYyespmjSZkEe7vwyfBW/Hx/J5rW9C7fmEVERKRc6VOb2F/g6UVCi5FEB3ByMPPF7W2o5+/OseQs7v5+Ixk5eWUYoIiIiEjFFBAQwLZt287Zv3XrVqpXr26HiKq2M61cPHr0KLTg6xkrj67kcMphPB09ubH+jeUdXsVzYh9s+cnY7v0y9H/f2N7yI8SsL7845j8L+xeDoxvcOg28ahQ6fDItm+dmbef6T1aw7mAizg5mHu3dkEVP9uSGFsFF/m8tIiIilYuS6GJ/Qc2M54ssLvpv3m6OfDeqHdXcHNl+NJnHp0ditdrKKEARERGRimn48OGMGTOGJUuWkJ+fT35+PosXL+bRRx9l2LBh9g6vSrHZbKQtP5NEL7of+k+7jITxwIYDcXM8t9K5yln6FtjyodG1ULsD1GoPrU73iZ/7hFEhXtbWTYQNXwEmGPQV1GhRcCgrN59vVx6k1/ilTFkXjdVmrNO0+KmePN63Ea5OdlwAVURERMrVJa12MmjQoAseT0pKupxYpKoqaOeyE6zWInsPFqVOdXcmjmjLbV+t46+d8bw9fzfPXRdehoGKiIiIVCyvv/46hw4donfv3jg4GB/trVYrI0aMUE/0cpa99x/y4uIwubjg1q7dOccPJB1g9bHVmE1mhocNt0OEFUzcdtgx09i+6oWz+/u8CrvmGMc3fgMd7i+7GP5ZCPOfOT3vKxB+PQC7YlOYviGGWVuOkpyZC0BEDS9eviGCDvV0h4eIiEhVdElJdG/vC/d58/b2ZsSIEZcVkFRBfg3B4gQ5qZAcDdXqFvvUdnV9eXdIcx6bHsnE5Qeo7evG7R3rlF2sIiIiIhWIk5MT06dP54033iAyMhJXV1eaNWtGnTr6PFTe0pYtA8C9Y0fMLi7nHJ+yewoAPUN6EuIZUq6xVUiL3zCemww6e2cqgLsf9H7JqERf/AY0GQgeAaU/f8Iuow+6zQotbye1zUP8sS6a6Rui2XokuWBYsLcLD1/VkFva1cJiVtsWERGRquqSkujfffddWcUhVZnFEfwbG9UmcTsuKYkOMKBVTQ6dTOfDhf/wwm87yMzJ597u9comVhEREZEKqGHDhjRs2NDeYVRpacuNJHpRrVySs5OZvX82YCwoWuVFr4O988FkgV7Pn3u8zSjYMhmObYG/X4RBX5bu/OknYMotkJ1CamB73sy+k9/fWkxmbj4AjhYTfSMCuaVdbbo28FPyXERERNQTXSqIwNPVJ8VcXPS/Hu3dkLu7hgLw5rxdjJu3C5tNPdJFRESkchs8eDDvvPPOOfvfffddhg4daoeIqqb85GQyt0QC4N7t3CT6rH9mkZmXSaNqjWgXdG6rlyrFZoNFrxnbLW8FvwbnjjFboP97gAm2TYNDq0pv/rxscqcMh6TDHDUF0f3wXUzbEk9mbj71/d15/rpw1oztzWe3taFHI38l0EVERASwcxJ93LhxtGvXDk9PTwICAhgwYAB79uy54DmTJk3CZDIVergUcbukXGGCTvdFj9teotNNJhMv9A/nmWvDAPhy+QGe+mUbufnW0opQREREpMJZvnw511133Tn7+/Xrx/LTi1xK2UtfvRry83FqUB+nkJqFjuVb85m6eypgVKGbTFU8KXtgCRxeabRz7PHM+cfVbANtRhrb856C/NzLnnrNvhOs++g2HI+uJ8XmxoisJ8lyrMaQNiHMeKATC5/owb3d6+Hn4XzZc4mIiEjlckntXErbsmXLGD16NO3atSMvL4/nnnuOq6++mqioKNzd3c97npeXV6Fke5X/IFoZ/Htx0RIymUw82LM+1T2cGPvrdmZuPsKpjBw+vbU1rk6WUgpUREREpOJIS0vDycnpnP2Ojo6kpKTYIaKqKW3pmVYuPc45tjRmKcfSj+Hj7MN1oef+4FGl2Gyw6HVju+3d4FPrwuN7vwxRsyEhCtZ9CZ0fLtG00SczeH1uFI33fMFTjgvIs5kZ7/0cd3W5hhtaBOPl4lii64qIiEjVYdck+vz58wu9njRpEgEBAWzatInu3c+9DfIMk8lEUFBQWYcn5elMEv3UQchOBWfPEl/q5ra1qO7uxOgpm1m8O4Hbvl7Lt6Pa4eN27hdMERERkStZs2bNmD59Oi+99FKh/dOmTSMiIsJOUVUtNquVtBUrAPDofm4S/cddPwIwpNEQXByq+B20u+fCsc3g6A7dnrj4eDdf6PsqzH4Elo6DpoPBq0axp0vPzuOzpfv4acUuXjR9w2BH43+n493e4LU+o0v6LkRERKQKsmsS/b+Sk41V0H19fS84Li0tjTp16mC1WmndujVvvfUWTZo0KY8Qpay4VwfPGpAaC/FRULvDZV2ud3ggP93TgTu/28Dm6CSGfLGGH+5qT7CPaykFLCIiImJ/L774IoMGDWL//v1cddVVACxatIgpU6YwY8YMO0dXNWTt3El+YiJmd3fcWrcqdGxP4h42xm/EYrJwS+Nb7BRhBWHNh8VvGNsdHwSPgOKd1/J22PwDHNkAfz8PQ7696Ck2m43ZW48xbt5uqqfu4lfHT6hnjsNmMmO66kVqdFMCXURERC5NhVlY1Gq18thjj9GlSxeaNm163nGNGzfm22+/5ffff+fHH3/EarXSuXNnjhw5UuT47OxsUlJSCj2kgipo6VKyvuj/1aaOLzMe7EyQlwv7EtIY/Plq9iWklsq1RURERCqCG264gd9++419+/bx0EMP8eSTT3L06FEWL15MgwZFLNgopS5tmdF73r1LF0yOhduC/LTrJwD61OlDkHsVv5N2+ww4vgtcvKHzI8U/z2yG68aDyQw7ZsKBZRccvuNoMkO/WMOj07bQL30Ws5xfNhLoXjUxjZpbvAp4ERERkf+oMEn00aNHs2PHDqZNm3bBcZ06dWLEiBG0bNmSHj168Ouvv+Lv78+XX35Z5Phx48bh7e1d8KhV6yJ998R+gi6/L/p/NQr0ZOZDnann705schZDvljDpsOnSu36IiIiIvbWv39/Vq1aRXp6OgcOHODmm2/mqaeeokWLFvYOrUpIW1Z0P/TErETmHpgLwO3ht5d7XBVKXg4sfcvY7vIYuPpc2vnBLY0e6mAsMpqXc86Qk2nZjP11OzdMWMn+w4f5zvk9XnacjBN5EHY9pgdWQp3Ol/MuREREpAqrEEn0hx9+mDlz5rBkyRJCQkIu6VxHR0datWrFvn37ijw+duxYkpOTCx4xMTGlEbKUhTOV6HE7SvWyNX1cmfFAZ1rW8iEpI5fbvl7Lkt0JpTqHiIiIiD0tX76ckSNHEhwczHvvvcdVV13F2rVr7R1WpZd34gRZ2427KN27dS10bM7+OeRYc4ioHkEL/yr+g8aWyXDqELgHQIf7S3aNq14Ad384sRfWflawOzffynerDtJr/FKmro+mgymKJR4v0Mu0GSzORhX7LT8a/dVFRERESsiuSXSbzcbDDz/MrFmzWLx4MaGhoZd8jfz8fLZv306NGkUvMOPs7IyXl1ehh1RQgf+qRLdaS/XSvu5OTLm3Az0a+ZOVa+WeHzYyc1PRLYBERERErgRxcXG8/fbbNGzYkKFDh+Ll5UV2dja//fYbb7/9Nu3atbN3iJVe2sqVALhEROAYULjH9/IjRpuX6+tdj8lkKvfYKozcTFj2rrHd/Wlwci/ZdVx9oO/rxvaydyD5CCv/OcF1H63g1T+iSM/K5i2f2Ux1ehOfvBPg1wjuXQTt74Wq/OcvIiIipcKuSfTRo0fz448/MmXKFDw9PYmLiyMuLo7MzMyCMSNGjGDs2LEFr1977TX+/vtvDhw4wObNm7n99ts5fPgw99xzjz3egpSm6g2MapHcdDh1sNQv7+bkwNcj2zKwVU3yrTae/GUrE5fvL/V5RERERMraDTfcQOPGjdm2bRsffvghx44d45NPPrF3WFXOmVYu7j26F96fk8am+E0AdA/pfs55Vcr6ryAtDrxrQ5uRl3etFsOgdifIzWDjlw9w+zfr+CchjXDXJNbU+IBbs6Zhwgat7oD7lkJQs1J5CyIiIiIO9pz8888/B6Bnz56F9n/33XeMGjUKgOjoaMzms7n+U6dOce+99xIXF0e1atVo06YNq1evJiIiorzClrJicYCAcIiNNKrRq9cv9SkcLWbeG9qC6u5OfL3yIG/N283u2FReG9AUD2e7/t9BREREpNj+/PNPxowZw4MPPkjDhg3tHU6VZMvLI33lKgA8/9MPfW3sWvJsedT2rE0drzr2CK9iyEqBle8b2z2fBQfny7rcseQspjnexxjbOtpmrKCnpQvXNvbm5mPvYD6VBE6ecMOH0GzIZYcuIiIi8m92zRrabLaLjlm6dGmh1x988AEffPBBGUUkdhfY9HQSfQdE3FgmU5jNJl64PoJALxfG/bmLX7ccZVP0KT4e1ooWtXzKZE4RERGR0rRy5Uq++eYb2rRpQ3h4OHfccQfDhg2zd1hVSmZkJNbUVCw+Prg0K1zxfKaVS5WvQl/zKWSeMlqrNL+lxJdJzsjls2X7mLTqENl5zng7XMPdDn/yjfunWA6kGoNqtoHB34DvpbcIFREREbmYCrGwqEiBoLJZXLQo93avx/T7O1HTx5XDJzMY/PlqPl+6H6v14j/uiIiIiNhTx44d+eqrr4iNjeX+++9n2rRpBAcHY7VaWbBgAampqfYOsdJLW2Ykyt27dcNksRTst9qsrDi6AoBuId3sEluFkH7SSKID9HrOuOv0EmXl5vPlsv10e3cxXy47QHaelfahvrQZ9S54BGLJOf33vMujcOd8JdBFRESkzCiJLhVLweKi28tlunZ1fZk3phv9m9Ugz2rjnfm7uf2bdcSnZJXL/CIiIiKXw93dnbvuuouVK1eyfft2nnzySd5++20CAgK48cayuatPDGf6oXv8p5XL7sTdnMg8gauDK20D29ojtIph1QeQkwpBzSH8pks6Nd9q4+eNMfQav5Rxf+4mJSuPxoGefDeqHdPv60jLBrVhyLfQ8Bq4fSb0fQ0cnMrojYiIiIgoiS4VTWAT4zkpGrKSy2VKbzdHJtzaincGN8PV0cLq/Se59sPlLIiKL5f5RUREREpD48aNeffddzly5AhTp061dziVWm5sLNl794LZjHuXzoWOnWnl0rFGR5wsVTSxm3LMWFAUoPdLYC7e106bzcbCqHj6fbSc/5uxjdjkLIK9XRg/tAXzHu1Gr7AATCaTMbhuV7jtZ2jQp4zehIiIiMhZWklRKhY3X/CqCSlHIT4K6nQql2lNJhO3tKtN27q+jJm6hZ3HUrj3h43c0bEOz/cPx8XRcvGLiIiIiFQAFouFAQMGMGDAAHuHUmmlLTfatbi2aIFDtWqFjlX5Vi42G/z9IuRlQe1OxU5yb44+xdvzdrP+UCIA3q6OPNyrAXd0qqPP4iIiImJ3qkSXiqegpUvZ90X/r/r+Hvz6UGfu7Wb0U5y89jA3TVjFnjj1FRURERERQ9pyo9rco0fhhUMTsxLZftxoS9itZhVNoq/6EHbMAJMZ+rwKZyrHz8Nms/Hpkn0M/nw16w8l4uxg5sGe9Vn+f724t3s9JdBFRESkQlASXSqegsVFy6cv+n85O1h4vn8E39/VHj8PZ/bEp3LDhJX8sOYQNpsWHRURERGpyqw5OaSvWQOc2w991dFV2LDRuFpjgtyD7BGefe2aAwtfNbavfQdqd7jg8LTsPB76aTP/+2sPNhsMbFWTpU/35Jlrw/B2dSyHgEVERESKR0l0qXgKKtF32jWMHo38mf9YN3o29icnz8pLv+/k3h82kpieY9e4RERERMR+MjZswJaRgYO/P85hYYWOrThShVu5xG6FX+8FbNDuHuhw3wWHHzyRzsBPV/HnjjgcLSbeGtiMD25pSQ1v1/KJV0REROQSKIkuFc+ZJHpCFFjz7RqKn4cz345sx4vXR+BkMbNwVwLXfbSCLdGn7BqXiIiISFn49NNPqVu3Li4uLnTo0IH169cX67xp06ZhMpmqRB/29NOtXNx7dD+7yCWQZ81j5bGVAHQP6V7kuZVWahxMHQ65GVCvl1GFfgGLd8dz44SV/JOQRoCnM9Pu68StHWqXU7AiIiIil05JdKl4qtcHB1fjQ3jiQXtHg9ls4u6uocwa3Zl6/u7EpWRxy5drmb4h2t6hiYiIiJSa6dOn88QTT/Dyyy+zefNmWrRowTXXXENCQsIFzzt06BBPPfUU3bpVjerrtGWn+6F3L5wo33Z8G6k5qXg5edHMr5k9QrOP3EwjgZ5yFPwawdBJYHEocqjVauPjRf9w9/cbSc3Ko22dasx5pCtt6lQrcryIiIhIRaEkulQ8ZgsERhjbUb/ZNZR/axLsze+ju3B1RCA5+Vaembmd52ZtJzvPvtXyIiIiIqXh/fff59577+XOO+8kIiKCL774Ajc3N7799tvznpOfn89tt93Gq6++Sr169coxWvvIOXyYnEOHwNER986dCx1bfsRIrnep2QUHc9FJ5ErHaoXfHoRjm8G1GgyfBq4+RQ5Nzcrl/h838f6CvdhscEfHOky5tyMBXi7lG7OIiIhICSiJLhVTu3uN5+X/g5P77RvLv3i6OPLF7W14sm8jTCaYsi6a4RPXEp+SZe/QREREREosJyeHTZs20adPn4J9ZrOZPn36sOb0IppFee211wgICODuu+8u1jzZ2dmkpKQUelxJzlShu7Vpg8XDo9Cx5UeNY1Wqlcuyt2HnLDA7wi0/GneUFmFfQhoDPl3Fgqh4nCxm3h3cnNcHNMXJQV9HRURE5MqgTy1SMbUYZvRTzMuCPx4Fm83eERUwm0080rsh345sh5eLA5ujk7j+k5VsPJRo79BERERESuTEiRPk5+cTGBhYaH9gYCBxcXFFnrNy5Uq++eYbvvrqq2LPM27cOLy9vQsetWrVuqy4y1va8qJbucSlx/HPqX8wYaJLcBd7hFb+ts+AZad7n1//AdTtWuSwBVHxDPh0FfuPpxPk5cLPD3Ti5nZX1v/uIiIiIkqiS8VkMsENH4KjGxxaAZt/sHdE5+gVFsDsh7vSONCT46nZDJu4lslrDmGrQAl/ERERkbKQmprKHXfcwVdffYWfn1+xzxs7dizJyckFj5iYmDKMsnRZMzLIOL3QqkePwkn0M61cmvs3p5pLFejvHbMBfnvI2O78CLS+45whVquNDxbs5d4fNpKWnUf7UF/+eKQrLWv5lG+sIiIiIqWgijTrkytStbpw1Qvw13Pw94vQ8GrwqmHvqAqp6+fOrw915v9mbmPutlhe/H0nW48k88aAprg4WuwdnoiIiEix+Pn5YbFYiI+PL7Q/Pj6eoKCgc8bv37+fQ4cOccMNNxTss1qtADg4OLBnzx7q1z+3tYezszPOzs6lHH35SF+7DltODo4hITj9p//7iqMrAOhWswosrpoUA9NuhfxsaHwd9Hn1nCEpWbk8Pi2SRbuNRWlHda7L8/3DcbSohktERESuTPoUIxVbhwcguDVkJ8OfT9s7miK5OzswYXgrxvYLw2yCGZuOcPOXazialGnv0ERERESKxcnJiTZt2rBo0aKCfVarlUWLFtGpU6dzxoeFhbF9+3YiIyMLHjfeeCO9evUiMjLyimvTUhxpy5cBRisXk8lUsD87P5t1seuAKtAPPTsVpg6D9AQIbAqDvgJz4cKR46nZ3PzFGhbtTsDJwcz4oS145cYmSqCLiIjIFU2fZKRiM1vgxk/A7AC7/oCo2faOqEgmk4n7e9Tnh7s64OPmyLYjydzwyUpW7z9h79BEREREiuWJJ57gq6++4vvvv2fXrl08+OCDpKenc+eddwIwYsQIxo4dC4CLiwtNmzYt9PDx8cHT05OmTZvi5ORkz7dSJgr6of+nlcvGuI1k5mXi7+pPmG+YPUIrH9Z8mHkvxO8A9wAYPg2cCy+uejQpk5u/XMPuuFT8PZ2Z8UAnhrQJsVPAIiIiIqVHSXSp+IKaQpfHjO15T0Nmkj2juaCuDf344+GuRNTwIjE9hzu+Wc/XKw6oT7qIiIhUeLfccgvjx4/npZdeomXLlkRGRjJ//vyCxUajo6OJjY21c5T2kZuQQN6xWDCbcWvXrtCxglYuId0KVahXOgtfhr1/gsUZhk0Bn8J3Gxw8kc7Qz1dz8EQ6NX1c+eX+TjQP8bFPrCIiIiKlzGSrYtm9lJQUvL29SU5OxsvLy97hSHHlZsEXXeHkP9B6JNz4sb0juqDMnHyem7WdWVuOAtC6tg9PXd2Yzg2Kv/CWiIiIVE76PHrWlfJnkbp0KUceeBCnBvWpP2dOwX6bzUb/Wf2JSY3hw54f0rtObztGWYY2/wCzHzG2B38DzYYUOrwrNoU7vlnPibRs6vm58+M9HQj2cbVDoCIiIiKXprifR1WJLlcGR5ezifPN38PBFfaN5yJcnSy8f3MLXr4hAhdHM5ujk7j163UMn7iWjYcS7R2eiIiIiFyC7F27AHCJiCi0/3DKYWJSY3AwO9AxuKM9Qit7J/fDnMeN7R7PnJNA3xJ9imET13IiLZvwGl78/EAnJdBFRESk0lESXa4cdTpD27uM7T/GQG7FXrjTZDJxZ5dQlj/di1Gd6+JkMbPmwEmGfLGGUd+tZ/uRZHuHKCIiIiLFkBUVBYBLeOEk+vIjRp/0NoFtcHd0L/e4ysXev8CaB7U7QY9nCx1as/8kt3+9juTMXFrX9mHavR3x83C2U6AiIiIiZUdJdLmy9HkFPIMh8QAse8fe0RRLgJcLr9zYhCVP92R4+1pYzCaW7jnODRNWcv/kjeyOS7F3iCIiIiJyAVlRRVein+mH3r1m93POqTQOGj8U0OhaMJ/9+rh4dzyjvltPek4+XRpUZ/LdHfB2c7RTkCIiIiJlS0l0ubK4eEP/94ztVR9D7Fb7xnMJavq4Mm5QcxY90YNBrWpiMsFfO+Pp99EKxkzdwoHjafYOUURERET+Iz8pidyjxjo3LuFhBfvTc9PZGL8RMBYVrZTy8+DwKmM79OwPBX9sPcZ9P2wiO89Kn/BAvhnZDndnBzsFKSIiIlL2lESXK0/YddBkINjyjQWO8vPsHdElqevnzvu3tOTvx7rTv1kNbDaYvfUYfd5fxlO/bCUmMcPeIYqIiIjIaVm7dwPgGBKC5V+LTa09tpY8ax61PGtR16uunaIrY3FbITsFnL2hRgsApm+IZsy0LeRZbdzUMpjPb2+Ni6PFzoGKiIiIlC0l0eXK1O9dcPExKtHXfmrvaEqkYaAnn97WmrljutInPACrDWZsOkKv8Ut56fcdpGTl2jtEERERkSrvoq1cQrpjMpnKPa5ycaaVS92uYLbwzcqDPDNzOzYbDG9fm/dvbomjRV8pRUREpPLTJx65MnkEwDVvGttL3oKT++0bz2VoEuzN1yPbMeuhznRr6Eee1cYPaw5z9fvLWbQr3t7hiYiIiFRpBYuKRoQX7LPZbKw4YiTRu9WspK1coCCJbgvtxkcL/+H1OcafxX3d6/HWwKZYzJX0xwMRERGR/1ASXa5cLW+D0B6QlwVzHgObzd4RXZZWtasx+e4OTLmnA3WruxGXksXd329kzNQtnEzLtnd4IiIiIlVS1q5zK9H3nNpDQmYCrg6utA1qa6/QylZeDhxeA8BXMTX5YOFeAJ7s24ix/cIqb/W9iIiISBGURJcrl8kEN3wEDq5GlcyWH+0dUano3MCPPx/tzv3d62E2Gf3S+36wnN8jj2K7wn8oEBEREbmSWDMyyDlwAACX8LOV6MuPGBXaHYI64GxxtktsZe7oRsjLJMOxGuM2GQnzF6+P4JHeDZVAFxERkSpHSXS5svmGwlXPG9t/Pw+plaP9iauThbHXhfPb6C6EBXmSmJ7Do9Miuef7jcQmZ9o7PBEREZEqIWvPHrDZcPD3x8Hfv2D/mSR6t5DK3MrFaFezOKsxNsy8emMT7u4aauegREREROxDSXS58nV4EGq0hKxkmP+svaMpVc1DfJj9cFee7NsIJ4uZRbsT6Pv+cn5adxirVVXpIiIiImXpTD9053/1Qz+VdYptx7cBxqKilVXa7sUArLY24c4udRnZua59AxIRERGxIyXR5cpncYAbPzG2d/4KJ/aV3VzRayFue9ldvwhODmYe6d2QuWO60qq2D2nZeTw/awe3fr2WQyfSyzUWERERkaqkoB/6v1q5rDq2Chs2GlZrSJB7kL1CK1Oxx0/iFLcRAGudrrzQP+IiZ4iIiIhUbkqiS+VQozk06mdsr/u8bOaIj4Lv+sFXV8GBpWUzxwU0DPRkxgOdeen6CFwdLaw9kMg1Hy5n4vL95OVbyz0eERERkcouO+rcRUVXHDHanHSvWTmr0NOz8/jk+yk4kcdxU3Wev+N6LGb1QBcREZGqTUl0qTw6PWQ8R06BjMTSv/6qj8BmhfwcmHorxGwo/TkuwmI2cVfXUP5+vDtdG/iRnWflrXm7Gfz5arbGJJV7PCIiIiKVlS0nh6x//gHOJtHzrfmsOrYKqJz90POtNh6dFklwkvE5161xLzxdnewclYiIiIj9KYkulUfdbhDUDHIzYNOk0r12UjRs/8XYrtECctPhp8EQt6N05ymmWr5uTL67Pe8Obo6niwNbjyRz06eruO+HjeyJS7VLTCIiIiKVSfb+/ZCbi9nLC8eaNQHYdmIbydnJeDl50cK/hZ0jLH3vzN/Nwl3xdLEYveDdG19l54hEREREKgYl0aXyMJmg42hje/1EyMspvWuvngC2fKjXE+78E2p1MBYynTwQTu4vvXkugclk4uZ2tVj4RA+GtAnBbIK/o+K59qPlPD49ksMn1S9dREREpKTOLCrqEh6OyWS0MznTyqVLcBcczA52i60sTFsfzcTlB/AggxbmA8bO0MpXbS8iIiJSEkqiS+XSdDB4BEFqLET9VjrXTD8Bm38wtrs+Dk7ucOvPENgM0hPghwGQfLR05iqBQC8Xxg9twd+Pd6d/sxrYbDBry1F6v7eM52ZtJy45y26xiYiIiFypsoroh778yHKg8rVyWb3/BC/8Ztxh+UbrVMy2fKhWF3xq2zcwERERkQpCSXSpXBycoP09xvaaCWCzXf41130JeZlQoyWE9jD2ufrAHb+Cb31IjobJA4xkux01CPDk09taM+eRrvRs7E+e1caUddH0+N8S3pwbRWJ6KVbmi4iIiFRyBZXoEeEAxKfHs+fUHkyY6FKziz1DK1UHjqfx4I+bybPauLFFMDd57TMOhFbOhVNFRERESkJJdKl82twFDq4QuxUOr768a2WnGq1hwKhCP30rLwAeATDid/AKgRN74cdBRosXO2ta05tJd7bn5/s70a5uNbLzrHy14iDd3lnM+wv2kpKVa+8QRURERCo0W34+WXv2AEY7F4AVR41WLs38muHr4mu32EpTUkYOd3+/keTMXFrX9uHdIc0xHTLeZ0HxiIiIiIgoiS6VkHt1aDHM2F7z6eVda9P3kJVkVJyH33DucZ9aMOI3cPMzkvZThkFOxuXNWUrah/ry8/2dmHRnO5oEe5Gek8/Hi/6h+7tL+GLZfjJz8u0dooiIiEiFlHM4GltGBiYXF5xCQ4HK18olJ8/Kgz9u5uCJdGr6uPLlHW1xyU2GuO3GgLqV432KiIiIlAYl0aVy6viQ8bxnXskX/szLOZuE7/IomC1Fj/NraLR2cfaG6NXw84jSXdT0MphMJno2DuCPh7vy2W2tqe/vTlJGLm//uZtu7y5m/F97OJaUae8wRURERCqUglYujRtjsljIteayNnYtUDmS6DabjRd/28GaAyfxcHbg21Ht8Pd0hkMrARv4h4FnoL3DFBEREakwlESXysm/ETS8GrDBui9Kdo3tP0PqMWOh0jOV7edTowXc9rPRRmbfAph1H1grTqW32WziumY1+Oux7owf2oKaPq6cSMthwpJ9dH1nMfdP3sjKf05gK40e8iIiIiJXuKxdp5PoTYxFRfee2ktmXiaejp6E+4bbM7RS8dWKA0zfGIPZBJ/c2orGQZ7GgYNGtb2q0EVEREQKUxJdKq9Oo43nLT9C5qlLO9dqhZUfnr2Og/PFz6ndEYb9CGZH2DkL5jxWOgubliIHi5khbUJY+nRPPr21NR1CfbHa4K+d8dz+zTp6v7+M71YdVN90ERERqdLOVKI7n+6HHpkQCUDzgOaYTVf2V6gFUfGM+3M3AC9dH0GvxgFnDxb0Q9eioiIiIiL/dmV/AhS5kNAeENgUcjOM3uaXYs9cOPkPuHhDm1HFP69BHxj8NZjMsPkH+PuFCpdIB3C0mOnfvAbT7+/E3493546OdXB3snDgeDqv/hFFhzcXMfbX7eyKTbF3qCIiIiLlymazkR21CwCXcKMSfWvCVgBa+re0V1ilYm98Ko9N24LNBnd0rMPIznXPHkyNh+O7ARPU7WqvEEVEREQqJCXRpfIymc72Rl/3JeQXs7raZoOVHxjb7e4FF69Lm7fJALjxE2N7zQRYMf7Szi9njQI9eX1AU9Y+15vXbmpCwwAPMnPzmbo+mn4frWDoF6uZvfUYOXlWe4cqIiIiUubyjh0jPzkZHBxwbtQQgMjjkQC0DGhpv8AuU3JGLvf9sJH0nHw616/OyzdEYDKZzg44U4Ue1AzcfO0TpIiIiEgFpSS6VG7NhoB7gNHbPOr34p1zaAUc3QQOLtDhgZLN2+p2uPZtY3vxG7Dtl5Jdpxx5ujgyolNd/n68O1Pv7ch1zYKwmE1sOHSKMVO30PntxbwzfzcHT6TbO1QRERGRMpO1y6hCd27QALOTE3HpccSmx2I2mWnu19zO0ZVMvtXGo9O3cOhkBjV9XJlwa2scLP/5KnhwmfGsVi4iIiIi51ASXSo3B2dof6+xvWZC8VqrnKlCb3UHePiXfO6OD0LXJ4ztv1+A7LSSX6scmUwmOtWvzme3tWHVM1fxaO+GBHg6cyItm8+X7qfX+KXc/MUaftkYQ3p2nr3DFRERESlVWWdauUScbuVy3Gjl0rhaY9wc3ewW1+V47+89LN1zHBdHM1/e0QZfd6dzB51ZVFRJdBEREZFzKIkulV/bu4yq8mNbIHrthccei4T9i8Fkgc4PX/7cPZ+FanUhLQ5Wf3L51ytnQd4uPN63EauevYrPbmtNz8b+mE2w/lAiT8/YRvs3F/LszG1sOnwKWwXs/S4iIiJyqc4sKuryn0VFW/i3sFdIl2Xe9lg+W7ofgHcGN6dpTe9zByVFw6lDxmfg2p3KN0ARERGRK4CS6FL5uftB81uM7TUTLjx21YfGc9PBRvL7cjk4Q59XT1/7I0g5dvnXtANHi5nrmtVg0p3tWfXsVTx9TWPqVHcjPSefaRtiGPz5avq8v4wvl+0nITXL3uGKiIiIlNiZdi4uTQpXol+J/dB3x6Xw1C9G/Pd1r8dNLWsWPfDg6X7oNVtf+npAIiIiIlWAkuhSNZxZYHT3XEg8UPSYk/vP9k3v+ljpzR1xE9TqCHmZsOj10ruundTwdmV0rwYsfaon0+/ryKDWNXFxNLP/eDrj/txNp3GLufeHjSyIiic3X4uRioiIyJUj7+RJ8uLjwWTCpXFjsvKy2HXSSKpfaUn0pIwc7vthExk5+XRt4Mf/XdP4/IPVykVERETkgpREl6ohIAwa9AFssO7Loses/hhsVmh4NQQ2Kb25TSa45i1je+sUo2VMJWAymehQrzrv39ySDc/3YdygZrSq7UO+1caCqHju/WEjXd9ZzC8bY7Ba1epFREREKr4z/dCd6tTB7O7OjhM7yLPl4e/qT7B7sJ2jK758q41Hpm4hOjGDWr6ufDK81bkLiZ5hs51NotftVn5BioiIiFxB7JpEHzduHO3atcPT05OAgAAGDBjAnj17LnreL7/8QlhYGC4uLjRr1ox58+aVQ7Ryxes02nje8iNkJhU+lhoHkVOM7a6Pl/7cIW2g2VBj++8XirfA6RXE08WR4e1rM+uhLix4vDv3dgulursT8SnZPD1jGzd9uooNhxLtHaaIiIjIBRX0Qz+9qGjk8UjAqEI3mUz2CuuSvfvXblb8cwJXRwsT72hLtaIWEj0j8QCkHgOLE9TqUH5BioiIiFxB7JpEX7ZsGaNHj2bt2rUsWLCA3Nxcrr76atLT0897zurVqxk+fDh33303W7ZsYcCAAQwYMIAdO3aUY+RyRarXCwIiICcNNv9Q+NjazyA/x/jiUFaLKfV+2Vjg9NAK2FN5f/hpGOjJ8/0jWD32Kp67LgxPZwe2H01m6BdrGD1lM0dOZdg7RBEREZEiFfRDjzAWFd2aYPQTv5IWFf1j6zG+XGa0L/zf0OaE17hIj/ODy4znkPbg5FbG0YmIiIhcmeyaRJ8/fz6jRo2iSZMmtGjRgkmTJhEdHc2mTZvOe85HH33Etddey9NPP014eDivv/46rVu3ZsKEiywYKWIyne2Nvu5LyM8ztrOSYeN3xnbXx41xZcGn1tlq+L9fhLycspmngnB2sHBf9/osebonw9vXxmSCudti6f3eMt77ew/p2Xn2DlFERESkkH9XottstkKV6FeCqGMpPD3DSPw/0KM+1zcvRgsa9UMXERERuagK1RM9OTkZAF9f3/OOWbNmDX369Cm075prrmHNmjVFjs/OziYlJaXQQ6qwZkPB3R9SjsCu04uIbvgGslPAPxwaXlO283d93Jg/cT9s/LZs56og/DycGTeoGXMf6UbHer5k51n5ZPE+eo1fyoxNR9QvXURERCqE/NRUcqOjAXAOD+dwymGSspNwMjsR4Rth5+gu7lR6DvdN3khWrpXujfx5+kILiZ5hs8HBFca2kugiIiIi51VhkuhWq5XHHnuMLl260LRp0/OOi4uLIzAwsNC+wMBA4uLiihw/btw4vL29Cx61atUq1bjlCuPoAu3uMbbXfAq5mbD2c+N118fAXMb/l3D2hF7PG9tLx0FG1ekTHhHsxdR7O/LF7W2o7etGQmo2T/2ylQGfrWKj+qWLiIiInWXv3g2AQ3ANHKpVK6hCb+rXFEeLox0ju7i8fCsPT93MkVOZ1KnuxifDWmExF+PuyoRdkHECHN2gZpuyD1RERETkClVhkuijR49mx44dTJs2rVSvO3bsWJKTkwseMTExpXp9uQK1vRssznB0E8x5HNITwLsWNB1cPvO3usOoes9KguXjy2fOCsJkMnFt0yAWPNGdZ/uF4eHswLYjyQz5Yg2PTN3C0aRMe4coIiIiVVRBK5fw04uKJkQC0CKg4vdDf2f+blbtO4mbk7GQqLdbMZP+Z1q51O4IDhdYfFRERESkiqsQSfSHH36YOXPmsGTJEkJCQi44NigoiPj4+EL74uPjCQoKKnK8s7MzXl5ehR5SxXn4Q/Obje2tU43nzo9AeVUYWRzgmjeM7fUT4eT+8pm3AnF2sPBAj/oseaonw9rVwmQyFsG6avxSxv66neV7j5Obb7V3mCIiIlKFZEWdXlQ0/PSioseN3uIt/VvaK6Ri+T3yKF+tOAjAe0Nb0DjIs/gnqx+6iIiISLHYNYlus9l4+OGHmTVrFosXLyY0NPSi53Tq1IlFixYV2rdgwQI6depUVmFKZXRmgU8At+pGdXh5atDHeFhzYeHL5Tt3BeLv6czbg5vzx8NdaR9q9Eufuj6aEd+up83rC3hieiR/7YwjMyff3qGKiIhIJffvRUVTclLYl7QPgBb+FbcSfU9cKv83YxsAo3vVp1+zGsU/2ZoPh1Ya20qii4iIiFyQgz0nHz16NFOmTOH333/H09OzoK+5t7c3rq6uAIwYMYKaNWsybtw4AB599FF69OjBe++9R//+/Zk2bRobN25k4sSJdnsfcgUKCDeS2PsWQocHwcmt/GO4+g3Yvxh2/QGHVkHdLuUfQwXRtKY30+/ryKp9J5m7PZYFUXGcSMvh1y1H+XXLUVwdLfRs7M+1TYPoFRaAl0vF7ksqIiIiVxZrVhbZBw4A4BIRzrrjRmK6tmdtqrtWt2do55VvtfHR9Hn8an6TY34tuarXl5d2gditkJ0Mzt4QVHF/KBARERGpCOyaRP/8c2NBx549exba/9133zFq1CgAoqOjMf9rscfOnTszZcoUXnjhBZ577jkaNmzIb7/9dsHFSEWKNPBL2Leo/Hqh/1dAOLQeCZu+g7+fh3sWl/3CphWYyWSia0M/ujb0440BTdl0+BTzd8Tx1844jiZl8ueOOP7cEYejxUSXBn5c2ySIvhGBVPdwtnfoIiIicoXL3rsX8vOx+PriEBhIZOQMAFoGtLRvYBfw07rD3HRiIk0sh2mSdhi+PQg3/wC+F7+7F4BDK4znOp2NdoMiIiIicl52/bRks9kuOmbp0qXn7Bs6dChDhw4tg4ikSnH3gxa32DeGXs/B9hlwbAts/8X+8VQQFrOJ9qG+tA/15cXrw9lxNIX5O2OZvyOO/cfTWbrnOEv3HOe5WdtpW9eXvuGB9A4PoJ6/h71DFxERkSvQv/uhm0yms4uKVtBWLrHJmcydP4/plo1YMWN29YG4bTCxh1Eo0rjfxS+ifugiIiIixVZ1y15FKgKPAOj2uLG96FXIybBvPBWQyWSiWYg3T18TxqIne7Lwie48fU1jmtX0xmqD9QcTeXPeLq56bxlXjV/Km3OjWHvgJHlamFRERESKKWvX6SR6RDh51jy2nTDauVTESnSbzcZLv+/kQes0AEzNb4YHVkBIO8hKhqnDYOErkJ93/ovk5cDhNca2kugiIiIiF6X79kTsreNDsPE7SI6BNZ9Cj6ftHVGF1iDAkwYBnozu1YCYxAwW7opn0a4E1h08yYET6RxYcZCvVhzEy8WBno0D6B0eQM9GAXi7qY+6iIiIFO3fi4r+c+ofMvMy8XD0oL53fTtHdq6/dsZxatcyejpvxWZ2wNTzGfAOgVHzYMGLsO4LWPkBHNkIg78Bz8BzL3JsM+Smg1t1CIgo9/cgIiIicqVREl3E3hxdoffL8Os9xhee1iOK/rIj56jl68adXUK5s0soqVm5rPjnBAt3xbNkdwKnMnKZvfUYs7cew2I20a5uNfqEB9I7PJBQP3d7hy4iIiIVhC03l+w9ewCjnUvkcaNCu4V/Cyxmiz1DO0dKVi4v/baDjx1/AcDU6nbwrWccdHCCfu9ArfYwe4zR8/zL7jD0O6Pv+b8dPN0PvW63Kr0mj4iIiEhx6ROTSEXQdDDUbGNUBC15w97RXJE8XRy5rlkN3r+5JRtf6MuMBzrxQI/6NAzwIN9qY+2BRN6Yu4te45fS76MV/Lj2MGnZF7jNWURERKqE7AMHseXkYHZ3x7F27bP90AMqXj/0d+fvpkHGZjqad2GzOEH3Iu5gbDoY7l0C/mGQFgeTrodVH8O/16M6uMx4Du1WPoGLiIiIXOGURBepCMxmuOYtY3vzZIjbUbbzpZ+ElGNlO4cdWcwm2tb15dl+YSx4ogfLn+7FS9dH0KVBdRzMJnbFpvDCbzvo8OZCnp+1nahjKfYOWUREROwka5fRysU5PAyT2czW41sBaOnf0o5RnWvjoUR+XHuYpxx+BsDU9i6jjUtR/BvBPYug2VCw5RttXqbfbvRMz82EmPXGuNAe5RS9iIiIyJVNSXSRiqJ2R4i4CbDB3Cfg5P7Sn+PUIZjzOLwfBh+1hJgNpT9HBVS7uht3dQ3lp3s6svGFPrzQP5x6fu6k5+Tz07porvt4BQM/W8WMTUfIys23d7giIiJSjv7dDz0hI4GjaUcxm8w082tm58jOysmzMvbX7fQyR9LavA8cXKHrExc+ydkDBn0F/d8DixPsngNf9jDW4snPBs8aUL1B+bwBERERkSuckugiFUmfV8HiDDHr4JM2MGUYHFha+Pbbkji+B2Y9AB+3ho3fQn6O8eXpl1GQkVgakV8xfNycuKdbPRY92YMp93agf/MaOJhNbIlO4qlfttLhrUW8PieK/cfT7B2qiIiIlIPsqF0AuIRHFFShN/RpiIeThz3DKuTLZfvZl5DCM04zjB0d7iveGjomE7S7B+6aD9614dRB+GuscSy0u3FcRERERC5KSXSRisQ3FEbNgQZ9ARvs/RN+uAk+7wybJkFOxqVd71gkTL8DPu0AW6cat/PW6wW3zQDf+pByBH69D6zWMngzFZvJZKJzfT8+vbU1q8dexdPXNKamjyvJmbl8s/Igvd9bxvCJa5mz7Rg5eVXvz0dERKQqsFmtZO3eDYBLRHhBP/SWAS3tF9R/7D+exieL93GNeSNhHAQnT+jy2KVdpGYbuH/Z6c+Yp9VVP3QRERGR4nKwdwAi8h+12sPtM+D4Xlj/JUROhYQo+ONRWPgKtBkF7e4F75rnv8bhNbBiPOxbeHZf2PXQ7QnjSxQYt/B+3Rv2LYCV70P3p8ryXVVoAZ4ujO7VgAd61Gf53uP8tO4wi3cnsObASdYcOImvuxPt6/rSqrYPrWpXo1lNb1ydLPYOW0RERC5TbkwM1rQ0TE5OONerR+SCSABa+FeMRUWtVhvP/bqdvPw8XvT8FXKBTg+Bm++lX8zNF279GdZMgKMbocmA0g5XREREpNJSEl2kovJvZPSwvOpF2DIZ1k+EpGhY+QGs+hgiboQODxpJd5PJaPmyfxEsfw+iVxvXMJmh6RDo+jgERhS+flBTuG48zH4YlrwJtTpAaNWuSLKYTfQKC6BXWABHkzKZvj6aaRtiSEjNZv7OOObvjCsYFxbkaSTVa1WjZW0fQqu7YzbrlmgREZErSdYuo5WLc6NGZJvyiTpp9EevKJXov2yKYd3BRG52WkvN3Ghw8YGOD5X8gmYzdBlTavGJiIiIVBVKootUdK4+0PkR4wvTnnmw9gs4vBJ2zjIewa2gySDYMRNiI41zzI7Q8lbo+hj41jv/tVvdDodXw9YpMOMueGBl8fprVgE1fVx54urGPNK7IZsOnyIyJokt0afYEp1EQmo2O4+lsPNYCj+ujQbA29WRlrV8aFnLh1a1fWhTpxqeLo52fhciIiJyIVk7zy4qGnUyijxrHtVdqhPiEWLnyOB4ajZvzt2FA3k87/47ZGIkwF197B2aiIiISJWjJLrIlcJsgfAbjEfsNlj3JWz/BY5tMR4ADq7Q9k7o9PCF272cYTIZ1e6xkUbLmJl3wx2/gUX/NJzhaDHTsV51OtarDoDNZiM2OYst0UlExhhJ9e1Hk0nOzGXZ3uMs23scAFdHCze0qMFtHerQPMQbkxbuEhERqXDOVKK7RIQTeTwSgFYBrSrEf7dfmxNFSlYeT1TfgHd6DLj5Qfv77R2WiIiISJWkTJnIlahGcxjwKfR9FTZ+B/sXQ51ORrW6u9+lXcvJDW7+ASb2hEMrYOk46P1imYRdGZhMJoJ9XAn2caV/8xoA5OZb2R2bypaYU0RGJ7HhcCIxiZn8vPEIP288QpNgL27rUIcbWwbj4ax/dkVERCoCm81GVtTpSvTwcCITvgEqRiuXJbsT+GPrMVxMuTzATGNntyfA2cO+gYmIiIhUUSabzWazdxDlKSUlBW9vb5KTk/Hy8rJ3OCIVx46ZRksXgNtmQMO+9o3nCmaz2dh4+BRT1kUzd3ssOXlWANydLAxoVZNbO9SmSbC3naMUERF70efRs+z5Z5EbH8++Hj3BYqHRxg1cNftaErMSmdxvsl0T6enZeVz9wXKOJmXyZaONXBP9vrEg/Jgt4Ohqt7hEREREKqPifh41l2NMIlKRNR0M7e41tn+9F5Jiynf+k/vhnbow5/HynbcMmEwm2tX15YNbWrJubG9e6B9OPT930nPy+WldNP0/XsmAT1fx88YYMnPy7R2uiIhIlXSmCt25XihHc4+TmJWIo9mRiOoRFzmzbH2wYC9HkzKp523m6pM/GTu7P6UEuoiIiIgdKYkuImdd86axUGnmKZhxJ+TllN/c6yca8278FhJ2l9+8ZayauxP3dKvHoid7MOXeDvRvXgNHi4nImCT+b8Y22r+1kFdm72RPXCpV7MYgERERuypo5RIRUdAPvUn1JjhZnOwW07YjSXy76iAAE8O3YEqPB+/a0GqE3WISEREREfVEF5F/c3CGoZPgy+5wZAMsfBmuHVf28+Zlw7bpZ1+vfB8GTSz7ecuRyWSic30/Otf343hqNr9simHq+mhiEjOZtPoQk1YfwtfdiYgaXkQEexU81/Nzx8Gi3ztFRERK25lFRZ3Dw4lMiATs2w/darXx/KwdWG0wtJkPDfZ8ZRzo+Qw42C+xLyIiIiJKoovIf1WrCwO/hKnDYO1nULsjRNxUtnPunmtUoTt7Q3YybJ8BPZ8F33plO6+d+Hs681DPBjzQvT4r9p1gyrrDLNqVQGJ6Div3nWDlvhMFY50czIQFeRZKrofV8NICpSIiIpepUCV63GwAWvq3tFs883bEsv1oMh7ODrwSsBz+SQTf+tB8mN1iEhERuZLk5+eTm5tr7zCkgnF0dMRisVz2dZSFEZFzNe4HXR6FVR/B7w9DYFOoXr/s5tvyo/Hc4T44uhn2L4KVH8KNH5fdnBWA2WyiRyN/ejTyJys3n73xqUQdSyEqNoWoYynsik0hPSefbUeS2XYkudC5daq70b6uL7d3rEOLWj72eQMiIiJXqLxTp8g7Fmts16/Fvl37AGgR0MI+8eRbef/vvQCM7lQd942fGwd6PQcWfWUTERG5EJvNRlxcHElJSfYORSooHx8fgoKCMJlMJb6GPpGJSNGuegliNkD0avhlJNy9oGwWtEqKgf2Lje2Wt0H93kYSPXIK9HgGvGuW/pwVkIujheYhPjQP8SnYZ7XaiDmVUSixHhWbQmxyFodPZnD4ZAa/bDpCy1o+jOpcl+ua1cDJQa1fRERELib7dCsXx1q12Jl1EBs2QjxC8HP1s0s8Mzcf4cCJdHzdnbjbPNe4My8gApoMsks8IiIiV5IzCfSAgADc3NwuK1EqlYvNZiMjI4OEhAQAatSoUeJrKYkuIkWzOMCQb+CLbhC3Hf58pmwqw7dOBWxQtxv4hhqPOl3g8CpY/Qn0e7v057xCmM0m6lR3p051d/o1O/sPfWJ6DjuOJjNry1HmbDtGZEwSj02P5I25u7i1Q21u61CbQC8XO0YuIiJSsZ3ph+4SEcGW41sAaBXQyj6x5Obz4cJ/AHiiczWc1n5pHOj1HJj147iIiMiF5OfnFyTQq1evbu9wpAJydTUKQhMSEggICChxaxd9KhOR8/MKhsFfAybY/D1s+7l0r2+1wpbJxnarO87u7/ak8bxpEqQdL905KwFfdye6N/Lng1tasvrZ3jzRtxGBXs6cSMvm40X/0OXtxTwydQubDidis9nsHa6IiEiFk3e6GsmlAiwq+uPaw8QmZ1HD24VhOTMhNx1qtICw6+0Sj4iIyJXkTA90Nzc3O0ciFdmZvx+X0zNfSXQRubD6vYxFPgEWvAS5WaV37UMrICnaWFA04sZ/zXkVBLeGvExY+2npzVcJ+Xs6M6Z3Q1Y+cxWfDG9F2zrVyLPa+GPrMQZ/voYbJqzkl40xZOXm2ztUERGRCiNw7FgabdyA1y1D2XZ8GwAt/Mu/H3padh6fLd0PwNvN4nBY/4Vx4KoXQbeii4iIFJtauMiFlMbfDyXRReTiuj4BXiGQGnu2crw0nFlQtNngwv3WTSbo/pSxvf5ryDxVenNWUo4WMze0CGbGg52Z80hXhrYJwcnBzI6jKTw9Yxudxi3i7T93M297LOsPJnLgeBopWbmqVBcRkSrL4uHBQdtxMvIycHd0p4FPg3KP4ZsVB0lMz6GX70m6b38GbFbj7rwGfco9FhERERE5P/VEF5GLc3CCbo/D3Cdh5QfQegQ4OF/eNTOTYNdsY7vV7eceb9TPWFArIQrWfwU9/u/y5qtCmtb05n9DWzD2unCmb4jhx7WHOZqUyRfL9p8z1tnBjJ+HM34eTqefnfHzPLvdKNCTxkGedngXIiIiZe9MK5fmfs2xmEvWH7OkEtNz+GrFAaqRwgTTu5iyU411Yfq/ryp0ERGRSm7ZsmXcf//9uLgUXs/MarXSo0cPPvnkEzp06EB2dvY556alpbFz506cnQvnZerWrcvhw4fPO+fIkSOZNGlSqcT/b2PGjGHVqlXs2LGD8PBwIiMjLzj+nXfeYfLkyTg4FE5L5+Tk8Pzzz9OxY0f69etXZIue0NBQZs2axcCBAzl48OA5xzMyMvjzzz+pX7/+Zb2noiiJLiLF0+oOWP4epBw1Ksjb3X1519sxA/KyIKCJ0brlv8xmozf6zLth7WfQ8SFw9ri8OasYX3cnHuxZn3u7hbJwVwJ/bDtGXHIWJ9KyOZGaTXpOPtl5Vo4mZXI0KfO81+nV2J/H+zaieYhP+QUvIiJSDiKPRwL26Yf++dJ9ZGdnMcVjAu7pMeBTB26ebBQviIiISKWWmZnJsGHDeOWVVwrtP3ToEM8+a7TUNZlMRSake/bsWeRd5Rs2bCA/32jlunr1agYPHsyePXvw8vICzi6wWRbuuusu1q1bx7Zt2y469tSpU0yYMIGePXsW2j9p0iRSU1PJzc2lc+fORSb8O3bsCEBsbGyRfzajRo26rL7nF6IkuogUj4MzdH0c/nzaqEZvdcflfcnbfGZB0dvPX23VZCAseQsS98PGb6HLmJLPV4U5WMxc2zSIa5sGFdqfmZNvJNTTsjmRllOQXD/zOiE1i83RSSzZc5wle47TOyyAx/s2omlNbzu9ExERkdJVsKiof8tynTc2OZPv1xziNYfvaJ63A5w84dbp4F69XOMQERGRysPf379g29fXF4CAgAB8fHwA+Pzzzxk/fjwxMTGEhobywgsvcMcddxScYzKZ+Oyzz5g9ezZLly6lRo0avPvuuwwZMuSC83788ccAHD9+vFhJ9CuVkugiUnytR8CK9yA5BiJ/grZ3luw6cdshNhLMjtD8lvOPM1uMxP3sh2HNBGh/b+He6XJZXJ0s1PJ1o5bv+VcxP3QinY8X/8NvW46yaHcCi3Yn0DcikMf6NKRJsJLpIiJy5TqReYIjaUcwYaKZf7NynfvjRfu43TaP4Y5LsJnMmIZ8CwHh5RqDiIiIVB2zZs3i0Ucf5cMPP6RPnz7MmTOHO++8k5CQEHr16lUw7sUXX+Ttt9/mo48+YvLkyQwbNozt27cTHq7PKVpYVESKz9EFuj5mbK94H/JySnadLT8Zz2HXXbziqvkt4F0L0uLPLkQq5aaunzvv39yShU/0YEDLYEwmWBAVT/+PV/LA5E3sjkuxd4giIiIlsjVhKwANqjXA06n81v84eCKd+E1/8LyD8bnG1Pd1aHR1uc0vIiJS2dlsNjJy8uzyKKrNSkUwfvx4Ro0axUMPPUSjRo144oknGDRoEOPHjy80bujQodxzzz00atSI119/nbZt2/LJJ5/YKeqKRZXoInJp2owy2rkkR8O2aUZ1+qXIyzbOA6MlzMU4OEGXR2HeU7DqI2N+i+OlRi2XqZ6/Bx8Oa8XDVzXk40X/8Me2Y8zfGcf8nXH0b1aDR/s0pFGgFiAVEZErx5l+6K38W5XrvD/NWcCHDh9jMdmMtnadRpfr/CIiIpVdZm4+ES/9ZZe5o167Bjenipdu3bVrF/fdd1+hfV26dOGjjz4qtK9Tp07nvD7Te7xfv36sWLECgDp16rBz586yC7gCUiW6iFwaR1cjqQ2wfDzkX+KCDXvmQeYp8AyG+lcV75xWt4N7gNFGZtv0S5tPSlWDAA8+Ht6Kvx7rTv/mNQCYuz2Waz5cziNTt7AvIdXOEYqIiBTPloQtQPkuKrrnwGHuOPA0XqZM0oPaQ/8Pzr82jIiIiEgF8vXXXxMZGUlkZCTz5s2zdzjlruL9NCIiFV+bO41q9KTDRlK71e3FP/fMgqItbzV6nheHoyt0fhgWvGS0kWkxvPjnSploFOjJp7e25pGrUvho4T/8uSOOP7YeY862Y9T2dSPQ04UAL2cCvVwIPP0c4OlCkLfxuiL+Mi8iIvDpp5/yv//9j7i4OFq0aMEnn3xC+/btixz71Vdf8cMPP7Bjxw4A2rRpw1tvvXXe8RVJdn42USejgHJcVDQvB9v026ljTuCEYw387ph6eYu0i4iISJFcHS1EvXaN3eauiMLDw1m1ahUjR44s2Ldq1SoiIiIKjVu7di0jRowo9LpVK+OuvZo1a5ZPsBWUshgicumc3KDzGFjwolGN3nwYWIrxz0nyEdi/2Nhuddulzdn2LiNxn7gfds6CZhdeHVrKR1iQF5/f3oaoYyl8uHAvf0fFc/hkBodPZlzwPE9nh4Ike21fN9rV9aVDPV9Cqp1/kVMRESlb06dP54knnuCLL76gQ4cOfPjhh1xzzTXs2bOHgICAc8YvXbqU4cOH07lzZ1xcXHjnnXe4+uqr2blzZ4X/krXr5C5yrbn4uvgS4hlS9hPabBz/+RHCsreRanMla8gUcPcr+3lFRESqIJPJpMKt/3j66ae5+eabadWqFX369OGPP/7g119/ZeHChYXG/fLLL7Rt25auXbvy008/sX79er755psLXnvfvn2kpaURFxdHZmZmQfuXiIgInJwqT8GA/kaJSMm0u9voUX7qIGz/BVoOv/g5kVMBG9TtBr71Lm0+Z0/o8CAsfcuoRm8yCMzl0JEqJwOiV0N2mtHPPS/rX89ZRbw+vV2nC3R8sOzjqyAigr2YOKItcclZHDmVQVxKFvEp2SSkZBGfkkVcShYJKdnEpWSRkZNPanYeqcfz2H88ndX7TzJtQwwAIdVc6RBanY71fOlYrzq1fJVUFxEpL++//z733nsvd955JwBffPEFc+fO5dtvv+XZZ589Z/xPP/1U6PXXX3/NzJkzWbRoUaEKpoooMiESMKrQTeXQTsW29nP8907DajMxM/QVRjVuXeZzioiIiJwxYMAAPvroI8aPH8+jjz5KaGgo3333HT179iw07tVXX2XatGk89NBD1KhRg6lTp55Trf5f99xzD8uWLSt4faZy/eDBg9StW7e034rdKIkuIiXj5A6dH4GFL8Py/0GzoReuRrdaYcvpVi6X0v7l3zrcB6s/gYSdsHc+hF1XsusUV/Q6mHW/8UPBpdr1B7j7V7mK+SBvo2XLhaRl5xGfkkV8chbxqVnsiUtj3cGTbDuSzJFTmRw5dYSZm48AUNPHlQ6nE+odQ6tTy9e1XJIdIiJVTU5ODps2bWLs2LEF+8xmM3369GHNmjXFukZGRga5ubn4+vqWVZilJjErEQeTQ/n0Q/9nIfz9PADvWm9j5MCRFzlBRERE5PL07NkTm81WaN+DDz7Igw9euNgvODiYv//++5LmWrp06aWGd0VSEl1ESq7dPUY1euJ+2DETWtxy/rGHVxo91J29IPzGks3nWg3a32O0dVn+P2jcr2wW48rLMSreV30ENquxqGn1BuDgDA4u5z47uhR+HbcDtk6B2WMgqDn4Nyr9GK9gHs4OePh7UN/fo9D+9Ow8Nh0+xdoDJ1l7wEiqH03K5NfNR/l181EAgr1d6FCvOn0jAukTHoiTg9bHFhEpDSdOnCA/P5/AwMBC+wMDA9m9e3exrvHMM88QHBxMnz59zjsmOzub7OzsgtcpKSklC/gyPdH2CR5q+RB51ryynej4Hmwz7sRkszI9ryf5HR+ihrdr2c4pIiIiIqVOSXQRKTlnD2PBz0Wvna5GH3L+BT/PLCjadLDRU72kOo6GtV/Asc1wYAnUv6rk1ypK/E749X6I3268bjEc+r0DLt7Fv4Y1H5Jj4NAK+GUk3LPo8t5zFeHu7ED3Rv50b+QPQEaOkVRfdyCRtQdOsvVIEseSs5i15SizthzF192JAS1rcnO7EMKCvOwcvYhI1fb2228zbdo0li5diovL+e9IGjduHK+++mo5RnZ+Lg4XvnPqsmUkwpRbMGWnsM4axjjLfSzu1bBs5xQREZErgre3N3PmzGHOnDnnHLvmGmNRVB8fH9q2bVvk+ebyaG9bRkJCQnjqqaeKPPbcc8/h6urKjh07inzvzZo1A4yFUs/3Z+PqWjYFCybbf2v7K7mUlBS8vb1JTk7Gy0tJF5HLlp0KHzaDzFMw+Jui25dkJsF7jY1e4fcshpA2lzfnn8/Aui+gTle4c+7lXesMaz6smQCL34D8HHCrDtd/CBElrJpPjYcvukJ6gtG+5qZPSyfOKiwzJ5/N0adY8c8JZm05QnzK2UrGFiHeDG1bixtbBuPl4mjHKEVELq4ifh7NycnBzc2NGTNmMGDAgIL9I0eOJCkpid9///28544fP5433niDhQsXnvfLzBlFVaLXqlWrQv1ZlJrTn1diTQH0z3yNEX3a8Fgf3Z0mIiJSmrKysjh48CChoaEX/CFfqrYL/T0p7mfzK/dnCxGpGJw9jepwgGXvGsno/9ox00igB0RAzVJYSKvzGDA7Gi1iDhevT+sFnToEk66HBS8ZCfRG18KDa0qeQAfwDIQh34DJDFt+hMgplx9nFefqZKFLAz+e7RfG6md7892odvRrGoSjxcTWI8m88NsO2r+5kMenR7Jm/0ms1ir1G7GIyGVxcnKiTZs2LFq0qGCf1Wpl0aJFdOrU6bznvfvuu7z++uvMnz//ogl0AGdnZ7y8vAo9KqXUONg0CYCnsu8Gdz/u6XaJi6qLiIiISIWhdi4icvk63AdrPoETeyDqN6Nly7/9e0HR0uhh7l0TWt4Km7+HFeOhzsySXcdmM2KbPxZy0sDJA64dB63uKJ04Q7tDz7Gw5E2Y8wTUaAmBF17VWorHYjbRKyyAXmEBnEzLZtaWo/y8MYa98WkF7V5q+7pxc9sQBrcJOaf/bL7VRmpWLsmZZx9JGWe3U7PyaBzkQf9mweq7LiJVxhNPPMHIkSNp27Yt7du358MPPyQ9PZ0777wTgBEjRlCzZk3GjRsHwDvvvMNLL73ElClTqFu3LnFxcQB4eHjg4eFx3nmqhFUfQV4W20yNWWVtygs96+PhrK9eIiIiIlcqfZITkcvn4m1Uoy99C5b9DyIGwpn+XHE74NgWo3K8+QUWHr1UXR8zEuD7FhptU4JaQFAzqNEcApuCy0Uq21Lj4Y8xsHe+8bp2ZxjwGfiGll6MAN2egug1sH+x0R/93iVGL3kpNdU9nLmnWz3u7hrK1iPJTN8Qwx9bjxGdmMH4v/fy/oK9tKjlQ26+1UiSZ+SSmp1HcZqZjZu3m5Gd63Jbh9r4uDmV/ZsREbGjW265hePHj/PSSy8RFxdHy5YtmT9/fsFio9HR0YX6b37++efk5OQwZEjhVm4vv/wyr7zySnmGXrGkxsPGbwH4X/ZAani7cnvHOnYOSkREREQuh3qii0jpyEyCD5tDdjIM/R6aDDD2zx8Laz+D8BvhlsmlO+fcp2DDV0UfqxZqJNSDmp1NsHsGGRXmUbPhj0chMxEsTnDVi9Bp9PkXRb1c6Sfgi26Qegya3QyDJpZOpbucV0ZOHn9uj2P6xhjWH0w87zg3Jwvero7nPFwcLfwdFVfQd93V0cKQNiHc1TWUUD/38nobIlJJ6fPoWZXyz+Kv52HNBHaYGnF95su8flNT7uhU195RiYiIVErqiS7FURo90ZVEF5HSs+QtWPYOBDSBB1aCNRfeCzOS1bf+Ao2uLt35bDZIioa4bRC3HWJPP6ccKXq8uz94hxiV8QCBzWDQlxDYpHTjKsrhNTCpP9jy4YaPoM2osp9TADh4Ip3tR5PxdHbA6z/J8gu1asnJszJn2zG+WnGQXbEpgPHbR5/wQO7tVo92dath0o8hIlIC+jx6VqX7s0hLMIoK8jIZmfMMO1zbserZq3BxLKMf6kVERKo4JdGlOEojia52LiJSejo+CGs/h4SdsHsO2KxGAt2zBtS/qvTnM5mgWh3jEX7D2f0Ziecm1k/sgfTjxsNkhi6PQc9nwcG59OMqSp1O0PslWPgyzPs/CG5tVMpLmQv1cy9R9biTg5lBrUMY2Koma/af5KsVB1iy5zgLouJZEBVP8xBv7ulW7/TipuqbLiIiwOqPIS+TPQ6NWJbVnMc71VUCXURERKQSUBJdREqPazXocD8s/x8sexc8Aoz9LW8FSzn+c+PmC/V6Go8zcjMhIQoSdp9t81LeOo8x+qPvnW/0R79v2cV7t4vdmUwmOjfwo3MDP/YlpPLNykP8uvkI244kM2bqFoK9XRjVpS7D2tfGw8mBrLx8MnLyyczJJzP339t5hfZn5uQT7ONKr7AALTYnIlIZpB2HDd8AMC7jJlwcLdzRSb3QRURERCoDfWsXkdLV8SGjGj1+O8Sf3tfyNruGBICjK9RsYzzsxWyGAZ/Dl90h8QDMfgSGTlJ/9CtIgwBPxg1qxlNXN+LHtdH8sOYQx5KzeGvebt7+czfWEjRIc3Iw07ORP9c1q0Hv8AA8XRxLP3ARESl7az6B3AwOODViaVZLRnaoha+7FqUWERGRcy1btoz777//nNYiVquVHj168Mknn9ChQweys7PPOTctLY2dO3fi7Fz4zvq6dety+PDh8845cuRIJk2aVCrx/9uYMWNYtWoVO3bsIDw8nMjIyAuOf+edd5g8eTIODoXT0jk5OTz//PN07NiRfv364ebmds65oaGhzJo1i4EDB3Lw4MFzjmdkZPDnn39Sv379y3pPRVESXURKl5svtL8PVr5vvK7TBaqX/j9eVyw3XyNx/u21EPUbrP8KOtxn76jkElX3cObRPg25v0c9fo88ytcrDvJPQlqhMS6OZlwdLbg5OeDqZMHV0YKrkwW309sujhYiY5I4eCKdv6Pi+TsqHieLme6N/LiuWQ36RATipYS6iMiVIf0krP8agDfTbsBsMnFPt3p2DkpEREQqqszMTIYNG8Yrr7xSaP+hQ4d49tlnAeOu6KIS0j179qSoJS43bNhAfn4+AKtXr2bw4MHs2bOnoM+3q6tr6b6Jf7nrrrtYt24d27Ztu+jYU6dOMWHCBHr27Flo/6RJk0hNTSU3N5fOnTsXmfDv2LEjALGxsUX+2YwaNYrc3NySvIWLUhJdREpfp4dh3ZeQmw6t7rB3NBVPSFvo+xr8NRb+eg5C7FwhLyXm4mjhlna1ubltLeJSsnC0GIlzV0cLZvPF7zCw2Wzsik3lzx2xzN0ey4Hj6SzclcDCXQk4Wkx0a2hUqPeNCMTbVQl1EZEKa80nkJtOjEsjFmW15vrmNajle271lIiIiEhZ8ff3L9j29fUFICAgAB8fHwA+//xzxo8fT0xMDKGhobzwwgvcccfZnI3JZOKzzz5j9uzZLF26lBo1avDuu+8yZMiQC8778ccfA3D8+PFiJdGvVHZdCW358uXccMMNBAcHYzKZ+O233y44funSpZhMpnMecXFx5ROw/H979x0eVZn2cfw76b0CqSSEKkUwEAmhSCgKWFEQWVkpilQFRFz7AuKKq6yKvIAFhV1XYAVFAQtNiIJ0DQICgoQiJaGlV5Lz/nEgMEIwQJJJMr/PdZ1r5vR75gn6zD3P3I9I6XgGQu8PoMOTcOOV/2Nrt9oMhxvuhKICWDAQcs7YOiK5DhaLhRBfd2p4ueLp6lSqBPr585qE+vDkbY1YNbYjy8bcwqguDahfy4uCQoNvd6cwbsE2Yl5ewcDZm/jf5kP8cjSd3ILCcn5FIiJSatmnzV+WAf/IuAuwMPQW/QpPRETEZgwD8rNss1xmhHhlsGjRIkaPHs2TTz7Jjh07GDp0KIMGDWL16tVWx7344ov06tWLbdu20a9fP/r27cuuXbtsFHXlYtOR6FlZWbRo0YKHH36Y++67r9TnXfxTBDC/VRGRSqZRD3ORy7NY4J7pkLwDzhyAz0dC349VH92OWSwWGgV70yjYm7G3NmRvcgZfbj/G19uPsyc5gzV7TrBmz4lzx0K4vzv1a3pRr6YX9Wp5Ub+W+Vz1d0VEKtj66ZCfyXH3BnyT25K4uoHcGO5r66hERETsV0E2vBJqm3s/dxRcPG1z7yuYMmUKAwcOZMSIEQCMHTuWDRs2MGXKFDp16lR83P3338/gwYMBmDRpEitWrGDatGnMmDHDJnFXJjZNovfo0YMePa4+yXbxTxFERKosdz+4/9/wwa2w50tY9jzc9rI5AWllkZ8NuWmQmwo5qRc9Xm5bqvnNe9tR0Px+m4VcXTQI8mZMkDdjujZkX0omX28/RsKvJ9ibkklaTgGHT+dw+HQOq88l1s/z93AuTqjXq+nFTRF+tIrwL/XoeBERuQrZp80SdsArWXcDFoZ2VC10ERERqVx27drFkCHW87G1a9eOqVOnWm2Li4u7ZP187fEePXrw/fffAxAZGcnOnTvLL+BKqErWRL/pppvIy8ujWbNmTJgwgXbt2tk6JBGRaxN6E9w+BZaMgg3TIeMY9JwJzm5/emq5OZsPOxbCD9Mg5ZerP3/JKKjdGvwjyz42O1W/lhePd2nA410aYBgGp7Ly+S0lk30nMvktJevcYyZHUnM4k13A5gNn2HzgQomg2gHu3BsdTq+WYUQGVr5RESIiVdaGmZCfwUnPBiw5Fc0Nwd50bFjzz88TERGR8uPsYY4It9W9q6lZs2aRk5MDgLOz/c3ZVaWS6CEhIbzzzjvExMSQl5fHrFmziI+PZ+PGjbRs2fKy5+Tl5ZGXl1e8np6eXlHhioiUTqsB4OQGX4yEnZ9BZjI88F/wCKjYOHLT4cd/w/oZkHFRh8PiaI6ad/MFN7/LPD+37u5njsY7tB6WPgF//VTlacqBxWKhhpcrNbxcia0baLUvO/8s+09k8du5pPrelEy+33uSw6dzeHvVXt5etZeYSH96tQrnjuYh+LjZX8dHRKTM5JyBje8A8FruPRg4MOSWulj0/z4RERHbslgqZUkVW2rcuDHr1q1jwIABxdvWrVtHkyZNrI7bsGED/fv3t1qPjo4GICwsrGKCraSqVBK9UaNGNGrUqHi9bdu2/Pbbb7z55pt89NFHlz1n8uTJTJw4saJCFBG5Ni0eAO9g+N9f4eA6+LAb9FtYMaO5M46bSYDNH0JemrnNK9ic/DT6ITOZX9qEQFAzmNkOflsF2xdA8z7lF7dcwsPFiWZhvjQLu1CLNye/kOW/HOfTH4+wdu8Jthw8w5aDZ5iweCe3NgmiV6twOtSvgZNjJSojJCJSFWx4B/LSSfWuz4ITNxHi68ZdLWxUf1VERETkCp566in69OlDdHQ0Xbt2ZcmSJXz22WesXLnS6rgFCxYQExND+/bt+fjjj9m0aRMffPDBFa+9b98+MjMzOX78ODk5OcXlX5o0aYKLS/WZs6tKJdEvp3Xr1qxdu7bE/c8++yxjx44tXk9PT6d27doVEZqIyNWp2xEe/gY+vh9O/mrWSn/wE7PkS3k4uRd+eBu2zYfCfHNbjYbnapr3ASfXq79mjQZwy1Ow+mX45hmo1wU8A//8PCk37i6O3HNTGPfcFEZyei6f/3SET3/8nV+TM1n68zGW/nyMmt6u9LwplF6twrkh2OfPLyoiYu9yUs1SLsDUgvswcODhdlE46wtJERERqYR69uzJ1KlTmTJlCqNHjyYqKorZs2cTHx9vddzEiROZP38+I0aMICQkhHnz5l0yWv2PBg8eTEJCQvH6+ZHrSUlJ1KlTp6xfis1U+SR6YmIiISEhJe53dXXF1fUaEkEiIrYQ1BQGrzQT6ck7YPbt0Off0ODWsrvH4U2wbirs/hIwzG21Y6HdGGjY/fonNm032ixLk/ILLH8e7n3neiOWMhLk48bQjvUYcktddh5NZ+HW31m87SgnMvJ4//sk3v8+iRuCvQn2vbqa/F6uTjQO8aFJiA+NQ3wI8nFVOQMRqd42vgt5aWT61GdOErsn5QAANYFJREFUSnO83Zzo21oDdURERKRyiI+PxzAMq23Dhw9n+PDhVzwvNDSU5cuXX9W91qxZc7XhVUk2TaJnZmayb9++4vWkpCQSExMJCAggIiKCZ599liNHjvCf//wHgLfeeouoqCiaNm1Kbm4us2bN4ttvv73qxhURqdR8QmHQ1/DJQ7B/Dcx9AO5806ydfq0Kz8K+FWby/ND6C9sb3W4mvSPaXHfYxZxc4O5pMKsrbJsHN94P9buU3fXlulksluKyL8/d3piEX0/w6dbfWbU7md3HM9h9POOqr7n052PFz/09nK2S6o1DfKhfywsXJ43QFJFqIDfNnAwceN/SCwMH+sVG4q15JkRERESqLZsm0bds2UKnTp2K18+XXRkwYABz5szh2LFjHDp0qHh/fn4+Tz75JEeOHMHDw4PmzZuzcuVKq2uIiFQLbj7w4AJYMspMRC8ZBWm/Q6fnSl+fvPAsHPgefvkcdi2F7JPmdgdnswZ721FQs9EVL3HNwmOg9RDY9K45yeiI9ZrYpZJycXLg1iZB3NokiDNZ+Xy/7yR5BYVXdY2TmfnsOpbOrmPp7D+ZxZnsAn747RQ//Haq+BhnRwv1a3nTOMSbJiE+REf4c2OYrxLrIlL1bHwPctPI8a3HtOQbcXF0YFC7OraOSkRERKoQX19fli5dytKlSy/Z161bNwD8/PyIiYm57PkO1/sLchsKDw9n3Lhxl9333HPP4e7uzo4dOy772m+88UbAnCi1pPfG3d297IK9iMX449j+ai49PR1fX1/S0tLw8VHdVxGp5AwDVr8C371mrrd4EO6aao72vpzCAjNxvvNz2L0Usi8kMXEPgOi/QpsR4FNyGawyk5cB09tA+u/Q9nG47eXyv6ct5WWAq7eto7C53IJC9iZnsutYOr+cW3YdSycj9+wlx7o5O9Aywp/YqEBaRwUQHeGHm7OjDaIWqVjqj15Q5d6L3HR460bITWVWred4+VAz+sSE81rvFraOTERExC7l5uaSlJREVFQUbm5XV5ZS7MeV/k5K2x+t8jXRRUSqNYsFOj8PvuHmiO5tcyHjKPT5yBytDmbiPCnhXOL8S8g5feF8j0BofBc0uQfqdADHCvypuas33PkGzO0D66dDs14QGl1x968oGcdh0VBI+h7uftv8osKOuTk7cmO4LzeG+xZvMwyDI6k57DqWwa5j6ew4ksaWg2c4nZVvNWLdxdGBFrV9aR0VQOuoQFpF+uPlqq6KiFQim96D3FTy/eox+bA5ydaQW+raOCgRERERKW/6ZCoiUhW0GmDWSv9kgFknfXYP6Pg07F12LnF+5sKxHjXMxHnTnhDZHhxt+J/6ht2g6X3mRKOLR8Gjq20bT1nbuwIWDbtQKmfpWAhuDiHNbRtXJWOxWAj39yDc34NbmwQBZmJ9X0omG5NOsynpNBuTTpGcnsfmA2fYfOAM01f/hqODhWahPrSOCqBlhD91anhSO8BDiXURsY28DFj/fwAs8n6QwuMOdG1ci/q19CskERERkepOn0JFRKqKBrfCoK/Mkd3JO8yJR8/zrHluxHlPiGxXuRLVPf4Jv30Lx382J2JrN7r871lYAOlHIPWQuRQVQtN7L4zev15n8+Hbl+CHaeZ6UDNz1H9SAnzSH4YmgJvvla9h5ywWCw2CvGkQ5M1f20RiGAaHTmezMek0G/efZtOBUxw+ncO239PY9nsakFR8bqCnC7UDPIgM9CAiwIPaAeZjRIAHwT5uODiUct4AEZGrsXkW5JzhrH9dJiTdAMCQW+rZOCgRERERqQiVKMsiIiJ/KvQmeGQF/K8fZJ6AxneeS5y3BYdKWkvaqxZ0+wd8MdKs7974Lgi4zp++/zFJ/scl/QgYRdbnrBwP7Z+Amx8FF49rv/fpJFj4MBz90Vy/+VGz3ntBNrx7C5xJgi8egz7/Kf0ksILFYiEy0JPIQE/6xNQG4GhqDpsPnGbD/tPsPJrG4dPZnMku4FRWPqey8kk8nHrJdVwcHQgPcCciwIMbw3yJqxtIy0h/1VoXketTkAM/mKPQlwc+RM4xC9ERftxcx9/GgYmIiIhIRdDEoiIiUv4MA/5zNyR9B1Edof8XV59gLiqEnz+BdW/ByV8vTZL/kaMr+EWYS+ohOLXX3O4VBLc8BS0HlDxBa0m2L4QlYyA/A9z84J7/M78UOO/3LfBhdygqgO7/hDbDru76V3J4M2ydbY7kr9mo7K5bxaTnFnDoVDaHT2dz6KLl8Olsfj+Tw9miS7s1Lk4OtIzwI65uDeLqBXJTbT9cnKrubPZS9ak/ekGVeS9+/AgWP0aRTzgx6VM4nVvEO39tSfdmFTBRt4iIiJRIE4tKaWhiURERqRosFrjzLZjZ1ix5sm0e3PRg6c41DNi7HFZOgJRfLmx3dAW/2ucS5ZEXEubnn3vWBIdzidLCs7D9E1gz2UyofzUO1r0N8U9D875/Xv4mPwu+fhp++shcr90Ges0y73+x8BhzVPo3T8PyF8z18JjSvc4r2b8G5v3FHO1+ZCsMSQBn++wg+rg50yzMl2Zhl5bLOVtYxPH0XA6dzibpZBZbDpzhh99Okpyex4b95oj2N1eCm7MDMZEBxNULJK5eIM3DfHFyVFJdREpgGLDxHQC21OrN6ZQiomp4cmuTYBsHJiIiIiIVRSPRRUSk4nz/BqyaCO7+MHIzeNW88vGHN8GK8XDoB3PdzRfaj4XmD5gjyh2uMvF5Nh9+/Dd8NwUyj5vbAhtAp+fMsjiXu17yTlgwCE7uASxwyzjo+EzJiXfDMOui71oMvrVh6HfgEXB1cV7s1+Xwv79CYd6Fbe3HQtfx135NO2IYBkkns1i//xTrfzvFhv2nOJmZb3WMp4sjN0cF0LZeILc0rEmjIG8sKsUj5Uj90QuqxHtxYC3MuQPDyZ0eju+yO82Jf9zbjH6xkbaOTERExO5pJLqURlmMRFcSXUREKk5hAbzXCZK3Q7Pe0PuDyx93Yg+segl2LzXXndwgdqhZ09y9DOrP5mebE8StfQNyzpjbgm+Ezi9Cg9vMkfOGAVs+gG+eMxPYXsFw33tQt+OfXz83Dd7taNZHb9gd+s67+oQ/wK6lsGCgWR6m0R3Q7D749BGwOMKjqyA0+uqvWRGKiiDnNKQfhYzjkHHM+jH7FMQ8DDf9pcJDMwyDvSmZrP/tXFI96RSp2QVWxwT5uNKxYU06NqxF+/o18PVwrvA4pXpTf/SCKvFezO8Hu5eSVKcPnXb3JNDThXXPdNZcCyIiIpVAVU2iJyQkMHTo0EtiLioqomPHjkybNo3Y2Fjy8vIuOTczM5OdO3fi6upqtb1OnTocPHiwxHsOGDCAOXPmlEn8F7vcAKR58+bRt2/fyx7/+OOPk5CQgMMfPiPn5uby7rvvApT5e6NyLiIiUrU4OsPdb8OsLrBjIbToCw1uvbA/7YhZciXxY7PmucUBbuoH8c+Cb1jZxeHiAe1GQauBsGGGOVnc8e0wtw+EtzZHm//0EexaYh5f/1a49x3wrFG667v5Qp9/w6xb4ddv4Iep5hcAV2PHp/Dpo2AUQtN74b73zfdv95ew8zP4fIRZ1uVq67qXtb0rzHI7xYnyc0tRwZXP+30zuHhCk7srJs5zLBYLDYO8aRjkzYC2dSgqMth1PJ31v51i7b6TbNh/iuT0PD7Z8jufbPkdBwvcVNuPjg1r0bFRTW4M88XRQaPURezGmYOw5ysAXj0dD8DAtnWUQBcREZHrkpOTQ9++fZkwYYLV9gMHDvDMM88A5meXxMTES86Nj4/ncmOiN2/eTGFhIQA//PADvXr1Ys+ePcWJYXd397J9EReZPXs23bt3L1738/Mr8dgTJ06wePFi6tSpY7V9woQJ5OTkAJT5e1MWlEQXEZGKFdYSYofDhumw9AkYscFMuK59Eza+C2dzzeNuuBO6/L18J9F084H4Z6D1EHPC0o3vwe+bzGQ6gIMzdJ0AbUZc/UjykBbQ45+wdAysmmQm5+u0K925iXPhi5HmFwnN+8I90y+Uj7n9dXOC1pRf4LvXofPzVxdXWcnPhmXPwtY5JRxgMevSeweDd4j146EN8PN8+HQweHwGddpXZORWHBwsNA31pWmoL4M71CW3oJDNB06TsOcECb+eYG9KJj8eSuXHQ6m8ufJX/D2c6dCgJrc0rMktDWtQy9uNwiKD3IJCczlbdOF5QSG5BUVWjw4OEBsVSKhf+XVgRaQMbX4fjCJOB7Vl2UE/PFwc+WsblXERERGRyqdmzQvlUgMCzJKitWrVKk5oz5w5kylTpnD48GGioqJ44YUXeOihh4rPsVgszJgxg8WLF7NmzRpCQkJ47bXX6N2795/e28/Pj+Dg6j1fjJLoIiJS8To/D7uXmJN8zn3ALO+Sm2bui2gLt06E2q0rLh6PALj1JTNZ/t0UMzHsG26Wmwlrde3XbTUQDv5gTmq68GEYtvbP68BvmW1+uYABLQeYE7JenMD3rAF3TDHLvKx9AxrfaSbsK1LyL7BwEJzYDVigZX+zHE5xojwEvGqZI+cvp2V/yM80y/XMexAe/hqCmlboSyiJm7MjHRrUpEODmrwAHE3N4btfzYT62r0nOZNdwOJtR1m87Sh1LUep6ZjJqUIP0gxP0vEkj9L9MuDGMF9ubRLErU2CuCFYNdhFKqX8LPjxPwC8m3cbAP1iI/D3tPEvgEREROSKDMMg52yOTe7t7uReKfv2ixYtYvTo0bz11lt07dqVpUuXMmjQIMLDw+nUqVPxcS+++CKvvvoqU6dO5aOPPqJv375s376dxo0bX/H6I0eOZPDgwdStW5dhw4YxaNCgSvk+XA8l0UVEpOK5eMKdb8J/e8HBtea2Wk3MUd/na5LbgnewmaDu8iI4e5ScBC4ti8V8nce2mROTfjYY/voZOJRQBmDju/D138znrYeaI9kv9140vRd2fGZOXvr5SHj024op62IYsOVDWPac+YsBr6BzdeLjr+46Do7QaxZ8dJ85aex/e8Ejy8EvolzCvh6hfu70bR1B39YRFBQWkXg4lYQ9JzixczX/SHsWJ0uRVW8q13Am3eJFJp5kOniT7eBFjqM3uY5e5Dr5kFrkRnJqJs7Hz0LyWdasLmSbK9QNcCHC14laHg44FBWYv84oLIDCfAiLgQ5jwcm15EBFpOxtmw+5aeR6R/Le8fq4ODowuENdW0clIiIifyLnbA6xc2Ntcu+ND27Ew9nDJve+kilTpjBw4EBGjBgBwNixY9mwYQNTpkyxSqLff//9DB48GIBJkyaxYsUKpk2bxowZM0q89ksvvUTnzp3x8PBg+fLljBgxgszMTEaNGlW+L6qCKYkuIiK2Ub+rWSd830poMxKa9yk5uVzR3HzL7lquXtDnP/B+J9i/xizBEv/MpcetfQtWjjeftx1ljoy/0pcJd/wLDqw1R/GvfRPiny67mC8n5wwsHmUm7sGsE99z5p+PrC+Jszv8ZS7Mvt0sTfPRffDwMvAMLLuYy5izowM31wng5mAn2DUdLEUUugdiMYqw5KVhMYpwsxTgxhlqcQaKMJezf7jQH3tfhcCJc8vl7Ftp1sLv/SHUbFjGr0pELsswzC82gYWOt2PgQO+YcIJ8qs6EZSIiIiLn7dq1iyFDhlhta9euHVOnTrXaFhcXd8n6+drjPXr04PvvvwcgMjKSnTt3Aubo9fOio6PJysri9ddfVxJdRESkzHSdYC7VXa0bzBHpi4bCmlehdizUO/dtv2FAwmuw5hVz/Za/Qafn/nw0vlctsz76p4+Yifkb7oDgZuUT/6ENZv3ytMPn6sSPN7/4uNo68X/k7g/9FsIHt8GpvWYt+gGLzV8qVGbLnjVLEflF4DhsnVlbv6gI8jMgJxVyU0t+zM8y30NHcynAicPpZ9l3Kp9fT+aSkW+hACfyccJwcKZxDWfuy5iLR/J2CmZ2YF39p9gXfi+Ojg44OVhwdDj/aMHJ0Xx0dXKkQS0vIgI8cNAkqCLXZv9qOLmHQmdP/nm8FY4OFobdUs/WUYmIiEgpuDu5s/HBjTa7d3U1a9as4ok/nZ1L/tV2bGwskyZNIi8vD1fX6vNrWiXRRUREKkKLvnBwnVlf99PBMOx7s3b4qpfM2uYAnV+EW8aV/prNepllXfZ8CV+MgMGrrr8EzcWKCs3YVk8GoxD8o8zR0GEty+4evmHw0GfwYTc4ssWs9d53btm+jrK0+0v46b+ABe5910ygg/mFgpvvuV8xlH7SQWeg7rmlc2ERWw+eYcUvyXy/K5mDp7LhGEylCW84z6QDO4j/dRKZu5bzXMFg0rnylw3erk40CfWhWZgvzcJ8aBbqS92aXjhWdGK98Kw5OWNmMsQ/q7I0UjWcG4We4HEbGRke3NsilIjAyvfTbBEREbmUxWKplCVVbKlx48asW7eOAQMGFG9bt24dTZo0sTpuw4YN9O/f32o9OjoagLCwsFLdKzExEX9//2qVQAcl0UVERCpOj9fgyI+QvMOcaDQ0Gjacqy3X7RWIG3l117NY4M43zOT8sW2wburVJeGvJP0YfPYoHDB/rseN98Mdb1xIGpelmo3gwU/g33fD3uVm2ZieM2xXG78kmSfM2ADajYLItmV6eSdHB2LrBhJbN5Dn72jM3pRMVu5KJjktl2WFM8hImUe34+9xp+NG2roe4MPg59nj0pTCIoOzRQaFRUWcLTTIzDvL3pRMMvLOsjHpNBuTThffw83ZgcYhZkK9WZgPTUN9aRjkjYvTdf6qoCQn9sCiYXD0R3P91G/QezY4qgsqldip3+DXZQBMSmkPwIh4jUIXERGRquupp56iT58+REdH07VrV5YsWcJnn33GypUrrY5bsGABMTExtG/fno8//phNmzbxwQcflHjdJUuWkJycTJs2bXBzc2PFihW88sorjBtXRp9LKxF9ghEREakozu5mffR3O8Kh9eYCcPsUaP3otV3TO9icgHTRUEj4p1nWpdaVZ07/U78ug8+HQ/YpcPY0J1tt8ZfyTWrXbg33z4H5D8K2uWa5mlsnlt/9rpZhwJJRkH0SgppBp+fL9XYWi4WGQd40DPK+aGsLOHI/LHyEgDNJjDsyBjo+DR3GXZKULigsYl9KJjuPprPjSBo7j6ax82g62fmF/HQolZ8OpRYf6+xooV5NL+rV9CKqhqe51PSkbg1P/DyuccLaokLYMNP8pUVhHrj6wtkcs6b+0tFw9/9Vvi9JRM7b9D5g8ItnG5JyQ+jWNIgGVv8WRURERKqWnj17MnXqVKZMmcLo0aOJiopi9uzZxMfHWx03ceJE5s+fz4gRIwgJCWHevHmXjFa/mLOzM9OnT+eJJ57AMAzq16/PG2+8waOPXuPn20pMSXQREZGKFFgP7plmli3BAndPg5YPXd81mz8AOxfBr9/A5yPgkRXXNtI3LxNWvwIbppvrwTeao4ZrNLi++EqrUXe46y1Y/Dise8v8gqDN8Iq595/56b+w5ytwdDHLuNiqJElYK7MU0FdPwbZ5sGYy7E+A+94Dv9rFhzk7miPOG4f40LtVOACFRQYHTmWdS6qbyfUdR9JIzz3L7uMZ7D6eccnt/D2cqXtRcr3uuQR7nUBP3JxLmAj49H74fCQc+sFcr9/V/Ds/shU+6W++l25+cNvLSqRL5ZObfq5kE7yWGg/AyE71bRiQiIiIyNWLj4/HMAyrbcOHD2f48Ct/vgoNDWX58uWlvk/37t3p3r37NcVY1SiJLiIiUtGa3guu3uDqY47Avl4Wizlx6fT1ZtmM9f8H7ceU/vy0I7DpXdgyB/LSzG2xw+DWlyo+Wdyyv1k7+9uX4ZtnwLMm3Ni7YmP4o9NJZixgjkAvrwlcS8vVG+59B+p1hqVjzWT1O+3MRHWTe0o8zdHhwojze24y6xkahsHvZ3LYm5LB/hNZJJ3MKn48np7LmewCth48w9aDZy65Xg0vV0L93AjxdSPE150QH1fiznxB051TcDybjeHihaXbP6DlAPNv1CfUHIH+xQjzb9TNDzo+VV7vksi12TYP8jNIcY1kTe6NdGhQg+bhfraOSkRERERsTEl0ERERW6jftWyv5xMK3SebCcrVr0Cj26Fmwyufc+RHsyb7zkVQdNbcFlAPuv0DGvUo2/iuRodxkJFsTka5aBh4BEK9TraJpajQLG2TnwkRbaHt47aJ43Ka94HwGHOi2vOjvFsNhG6TwaV0EylZLBZqB3hQO8CDzjdY78vKO8uBU2ZCPel8gv1kFvtPZJKee5aTmXmczMzj59/TCOEU/3R+j+aO2wFYX9iEv2UMpWBZbUI2/kCorzvh/u7Ur3ULbWP/TtjGl2D1y+ZErLFDyviNEblGRUXFE4rOyO4CWHhMo9BFRESkHPj6+rJ06VKWLl16yb5u3boB4OfnR0xMzGXPd3AopzmNKkC9evXo3fvyA6XOv/bK+N5YjD+O7a/m0tPT8fX1JS0tDR+fcpgcTURExFYMAz6+H/atgPCb4eFl4PCHkhtFhbDna1g//UK5DYA6HcyJTRt0g8rQISsqhIWD4JcvwMULBn4Jwc3NkfI5qZCbevnHnDPm8/wsqNcFYode+h5cjbVvwcrxZgzD14F/net9ZWWvsABW/8OMFQMCG5hf0ngGgkcN8Kxx0WOgOQL8OtrYMAzOZBdwNDWHY6k5eOz6H612/RO3wizycWGG80NMz+pMQVHJpVqecFrIaKfPAPgiajx5Te+nQS0v6tfywtvN+ZpjqyrUH72gUr0Xvy6HufeT6+hFy6y3aRIZwoJhcVhUdkhERKTSys3NJSkpiaioKNzc3GwdjlRSV/o7KW1/VCPRRUREqguLxawpPr0N/L7ZHGV+fuR0XiYkfmxO9ngmydzm4ATNekPcCAhpYbOwL8vBEe59D7JPw4Hv4f3OYBQBV/Hd/76VZhL+3pkQUPfqYzi+w0xOA3R/tXIm0AEcnaHrBKgbD58NhVN7zaUkFkczmX4+qe5ZA7yCwCcMfMMvLF5Bl/0CwmKxEODpQkDRGZoljIFfvzZ3hN+MS8+ZjKnRgFFFBicz8zialsux1ByOpuVy8FQWe5Mz2ZuSyZuZvfAhi0FOy7hj/ySG7cnkb0WtAAj1daN+kDcNapm12CMDPYgM8CTUzw0nx0rwBY9UXxtnAjD/bEeycWNkp/pKoIuIiIgIoJHotg5HRESk7G39NywZBU5u0G+BmUzeOgdyz9U7d/ODmIeh9aNmGZjKLDcN/tPTrPV+npM7uPuBu7/5Wtz9Ln0syIbv/gX5GeDsCbdNMl9zaRNiZ/PgvU6QstMsjdN3btWYBDPrFOxYCOlHIfukuZ59ErJOQvYpyEsv/bUcnMA7xEyoX5xg9wkzR/wvf958dHCGTs9B21GlntD2TFY+e5PTqbFqDHWPLCEfZ0Y7Ps/XWSWXIHJysBDm705EgEdxYj0i0HweEeCBh4v1vXPyC0nNyedMVgGp2fmk5hRwJjuf1OwCzmSZ66nZ+YT5uTPxnoqtc6/+6AWV5r04sQemt8bAQoe8N/EJrs+Xo9oriS4iIlLJaSS6lIZGoouIiMilWvY365zvXw3/vuvC9oB65qjzFn8BF0/bxXc13Hxh8Cpz9LyLl5kgL+1kp03vg89HwMG18OVY2POVOflmab44+PZlM4HuUQPuertqJNDBLOESO7Tk/WfzzGR61knrJHvGMXOC2fQjkPa7mYQvOgtph82lJMHNzUlOg5peVZj+ni60rlsDIufAJ/1x2fMlMx2nkPnoZ+xxasCvyZnsTc7k0OksDpzK5tDpbPLPFnHwVDYHT2Xz/WUG2tf0diXAw4W0c8nyvLNFpYrlhmDvq4pdqqlztdBXE8PvRi2maxS6iIiIiFxESXQREZHqxmKBu9+Gme3MkceVrd751XJwgMB6V3+efyQMWAIb34FVE80R+TPi4I5/QbNeJSfGD6yDH6aZz+9+G7xqXnvslY2Tq/klwp99kVBUCBnHzyXVD5sJ9rTfLyTZc1OheV/o8CQ4uVx7PI5O0PtDmHs/JH2H18IHaDXoG1q1tp7ltKjIIDkjl4Onsjl0KpuDp7PM56fNpHpaTgEnMvI4kZFn/XIdLPh5uODn4Yy/h7P53N0Zf09zm5+7CyF+GrFk93JSYds8AN7Pv426NT3p3izYtjGJiIiISKWiJLqIiEh15BcBw3+AghyoWXKJjGrPwcEcfV+/CywaCkd/gk8fgV1L4I43zJHbF8tNh8+HAQZE/xVuuMMmYducgyP4hplL7dbley9nN7Nczn/ugSNb4aOe8PA3VjXoHRwshPi6E+LrTpu6gZdcIi27gIOns0jLKcDP/VyC3MMZL1cnjSaWP/fTR1CQzT4iWF/UhNc71sPRQX83IiIiInJBFRyOJiIiIqXiV9u+E+gXq9kIHlkB8c+atb5/+RxmxsGvy6yPW/YspB4yv4ToNtkmodolV2/otxBq3mCWlvlPT3MkfCn5ejjTPNyPDg1qcmO4L7UDPPB2c1YCXf5cUSFseg+A9wu6EebnQc/oMBsHJSIiIiKVjZLoIiIiYh8cnSH+GRi8Emo0gsxkmNsHvnjMHIG++0v46b+ABe59F9zse8LHCucRAA8tMr/AOJMEH90H2adtHZVUd3u+htRDpOHFF4VtGdqxLs6O+ogkIiIiItZUzkVERETsS2g0DE0wJw9dP90s5ZCUAPnZ5v52oyCyrW1jtFc+odD/C/iwuzmx6/RYuGUctBpY+gllRa7GxncA+PhsZ7y8fOgTU9vGAYmIiIg9SEhIYOjQobi5Wc/PU1RURMeOHZk2bRqxsbHk5eVdcm5mZiY7d+7E1dW6f1ynTh0OHjxY4j0HDBjAnDlzyiT+i13u15/z5s2jb9++lz3+8ccfJyEhAYc/zNeVm5vLu++ak72X9XtTFpREFxEREfvj7A7d/gGNesDnw80SLgBBzaDT87aNzd4F1DVHpM9/EM4cgK//Zk702vFpaPEXczJSkbJwfAcc+J5CHPjo7K0M7hCFm7OjraMSERERO5CTk0Pfvn2ZMGGC1fYDBw7wzDPPAGZyOjEx8ZJz4+PjMQzjku2bN2+msLAQgB9++IFevXqxZ88efHzMX9i6u7uX7Yu4yOzZs+nevXvxup+fX4nHnjhxgsWLF1OnTh2r7RMmTCAnJwegzN+bsqDfKoqIiIj9qtPenIA15mEIuhHue18jniuDoKYwcrM5+at3CKQdhsWPwYxY2PEpFBXZOkKpDjaZI52+LryZLLcg+sVG2DggERERkWtXs2ZNgoODCQ4OJiAgAIBatWoVb5s7dy716tXDxcWFRo0a8dFHH1mdb7FYmDlzJj169MDd3Z26deuycOHCUt3bz8+v+D7BwcGXjCKvDpREFxEREfvm6g13vgnD10JQE1tHI+c5ucDNj8Con+C2l8E9AE7tg4UPw7u3wJ5voJxGmYgdyDqF8fMnAMw5242B7aLwdnO2cVAiIiJSFgzDoCg72yZLeY2Cvl6LFi1i9OjRPPnkk+zYsYOhQ4cyaNAgVq9ebXXciy++SK9evdi2bRv9+vWjb9++7Nq160+vP3LkSGrUqEHr1q358MMPK+37cD30e1gRERERqbyc3aHt49ByAGyYaZZ2Sd4O8x6A8NbQ5UWIusXWUUpV8+O/sZzNZXtRHX5xbsL7bevYOiIREREpI0ZODntatrLJvRv9uBWLh4dN7n0lU6ZMYeDAgYwYMQKAsWPHsmHDBqZMmUKnTp2Kj7v//vsZPHgwAJMmTWLFihVMmzaNGTNmlHjtl156ic6dO+Ph4cHy5csZMWIEmZmZjBo1qnxfVAVTEl1EREREKj83H4h/Glo/Cuvego3vwe+b4N93Qd146Px3CL/ow9LZfMhKgcxkyEyBjOPmY2byhW2Zx6FGQ/jrp7Z6VWIrFgtZFk/mnO1Ov3aR+Hu62DoiERERkXKza9cuhgwZYrWtXbt2TJ061WpbXFzcJevna4/36NGD77//HoDIyEh27twJmKPXz4uOjiYrK4vXX39dSXQREREREZvxCIBbX4I2I+C7KbB1DuxfYy6h0VCQYybJc86U7npO1a9eo/y5jaH9GZQTgcXRhW871LV1OCIiIlKGLO7uNPpxq83uXV3NmjWreOJPZ+eSy+DFxsYyadIk8vLycHWtPvNNKYkuIiIiIlWPdzDcMcUs9ZLwT9g2D47+ZH2MgxN4BYFXrXOPQdbr3sHmInbng7VJZONGv5gIgnz0RYqIiEh1YrFYKmVJFVtq3Lgx69atY8CAAcXb1q1bR5Mm1nNCbdiwgf79+1utR0dHAxAWFlaqeyUmJuLv71+tEuigJLqIiIiIVGX+kdBzBrQfC8cSwSPwQoLczQ8cHGwdoVRCr/duQbP1B+h5U+k+DIqIiIhUZU899RR9+vQhOjqarl27smTJEj777DNWrlxpddyCBQuIiYmhffv2fPzxx2zatIkPPvigxOsuWbKE5ORk2rRpg5ubGytWrOCVV15h3Lhx5f2SKpyS6CIiIiJS9dWoby4ipeDr4cyoLg1sHYaIiIhIhejZsydTp05lypQpjB49mqioKGbPnk18fLzVcRMnTmT+/PmMGDGCkJAQ5s2bd8lo9Ys5Ozszffp0nnjiCQzDoH79+rzxxhs8+uij5fyKKp6S6CIiIiIiIiIiIiLVRHx8PIZhWG0bPnw4w4cPv+J5oaGhLF++vNT36d69O927d7+mGKsa/b5VRERERERERERERKQEGokuIiIiIiIiIiIiUkF8fX1ZunQpS5cuvWRft27dAPDz8yMmJuay5ztU4Xl/6tWrR+/evS+77/xrr4zvjcX449j+ai49PR1fX1/S0tLw8fGxdTgiIiIiYmfUH71A74WIiIhcj9zcXJKSkoiKisLNzc3W4UgldaW/k9L2R6vu1xYiIiIiIiIiIiIiIuVMSXQRERERERERERERkRIoiS4iIiIiIiIiIiJVlp1Vq5arVBZ/H0qii4iIiIiIiIiISJXj7OwMQHZ2to0jkcrs/N/H+b+Xa+FUVsGIiIiIiIiIiIiIVBRHR0f8/PxISUkBwMPDA4vFYuOopLIwDIPs7GxSUlLw8/PD0dHxmq+lJLqIiIiIiIiIiIhUScHBwQDFiXSRP/Lz8yv+O7lWSqKLiIiIiIiIiIhIlWSxWAgJCaFWrVoUFBTYOhypZJydna9rBPp5Nk2if/fdd7z++uts3bqVY8eOsWjRInr27HnFc9asWcPYsWPZuXMntWvX5oUXXmDgwIEVEq+IiIiIiIiIiIhUPo6OjmWSLBW5HJtOLJqVlUWLFi2YPn16qY5PSkrijjvuoFOnTiQmJjJmzBgGDx7MsmXLyjlSEREREREREREREbFHNh2J3qNHD3r06FHq49955x2ioqL417/+BUDjxo1Zu3Ytb775Jt26dSuvMEVERERERERERETETtl0JPrVWr9+PV27drXa1q1bN9avX1/iOXl5eaSnp1stIiIiIiIiIiIiIiKlUaUmFj1+/DhBQUFW24KCgkhPTycnJwd3d/dLzpk8eTITJ068ZLuS6SIiIiJiC+f7oYZh2DgS2zv/HqhvLiIiIiK2UNq+eZVKol+LZ599lrFjxxavHzlyhCZNmlC7dm0bRiUiIiIi9i4jIwNfX19bh2FTGRkZAOqbi4iIiIhN/VnfvEol0YODg0lOTrbalpycjI+Pz2VHoQO4urri6upavO7l5cXhw4fx9vbGYrGUa7x/lJ6eTu3atTl8+DA+Pj4Vem+xDbW5fVK72x+1uX1Su9ufsmpzwzDIyMggNDS0DKOrmkJDQ9U3lwqjNrc/anP7pHa3P2pz+1TRffMqlUSPi4vjq6++stq2YsUK4uLiSn0NBwcHwsPDyzq0q+Lj46N/1HZGbW6f1O72R21un9Tu9qcs2tzeR6Cfp7652ILa3P6oze2T2t3+qM3tU0X1zW06sWhmZiaJiYkkJiYCkJSURGJiIocOHQLMUiz9+/cvPn7YsGHs37+fv/3tb+zevZsZM2bwySef8MQTT9gifBERERERERERERGp5myaRN+yZQvR0dFER0cDMHbsWKKjo/n73/8OwLFjx4oT6gBRUVF8+eWXrFixghYtWvCvf/2LWbNm0a1bN5vELyIiIiIiIiIiIiLVm03LucTHx19x5tM5c+Zc9pyffvqpHKMqP66urowfP96qRrtUb2pz+6R2tz9qc/ukdrc/avPqRe1pf9Tm9kdtbp/U7vZHbW6fKrrdLcaVstgiIiIiIiIiIiIiInbMpuVcREREREREREREREQqMyXRRURERERERERERERKoCS6iIiIiIiIiIiIiEgJlESvQNOnT6dOnTq4ubkRGxvLpk2bbB2SlJHvvvuOu+66i9DQUCwWC59//rnVfsMw+Pvf/05ISAju7u507dqVvXv32iZYKROTJ0/m5ptvxtvbm1q1atGzZ0/27NljdUxubi4jR44kMDAQLy8vevXqRXJyso0ilrIwc+ZMmjdvjo+PDz4+PsTFxfH1118X71ebV3+vvvoqFouFMWPGFG9Tu1c/EyZMwGKxWC033HBD8X61edWnfnn1pr65/VHf3P6oXy7ql9uHytQvVxK9gvzvf/9j7NixjB8/nh9//JEWLVrQrVs3UlJSbB2alIGsrCxatGjB9OnTL7v/tdde4+233+add95h48aNeHp60q1bN3Jzcys4UikrCQkJjBw5kg0bNrBixQoKCgq47bbbyMrKKj7miSeeYMmSJSxYsICEhASOHj3KfffdZ8Oo5XqFh4fz6quvsnXrVrZs2ULnzp2555572LlzJ6A2r+42b97Mu+++S/Pmza22q92rp6ZNm3Ls2LHiZe3atcX71OZVm/rl1Z/65vZHfXP7o365fVO/3L5Umn65IRWidevWxsiRI4vXCwsLjdDQUGPy5Mk2jErKA2AsWrSoeL2oqMgIDg42Xn/99eJtqamphqurqzFv3jwbRCjlISUlxQCMhIQEwzDMNnZ2djYWLFhQfMyuXbsMwFi/fr2twpRy4O/vb8yaNUttXs1lZGQYDRo0MFasWGF07NjRGD16tGEY+rdeXY0fP95o0aLFZfepzas+9cvti/rm9kl9c/ukfrl9UL/cvlSmfrlGoleA/Px8tm7dSteuXYu3OTg40LVrV9avX2/DyKQiJCUlcfz4cav29/X1JTY2Vu1fjaSlpQEQEBAAwNatWykoKLBq9xtuuIGIiAi1ezVRWFjI/PnzycrKIi4uTm1ezY0cOZI77rjDqn1B/9ars7179xIaGkrdunXp168fhw4dAtTmVZ365aK+uX1Q39y+qF9uX9Qvtz+VpV/uVOZXlEucPHmSwsJCgoKCrLYHBQWxe/duG0UlFeX48eMAl23/8/ukaisqKmLMmDG0a9eOZs2aAWa7u7i44OfnZ3Ws2r3q2759O3FxceTm5uLl5cWiRYto0qQJiYmJavNqav78+fz4449s3rz5kn36t149xcbGMmfOHBo1asSxY8eYOHEiHTp0YMeOHWrzKk79clHfvPpT39x+qF9uf9Qvtz+VqV+uJLqIyHUaOXIkO3bssKrLJdVXo0aNSExMJC0tjYULFzJgwAASEhJsHZaUk8OHDzN69GhWrFiBm5ubrcORCtKjR4/i582bNyc2NpbIyEg++eQT3N3dbRiZiIj8GfXN7Yf65fZF/XL7VJn65SrnUgFq1KiBo6PjJbPDJicnExwcbKOopKKcb2O1f/X02GOPsXTpUlavXk14eHjx9uDgYPLz80lNTbU6Xu1e9bm4uFC/fn1atWrF5MmTadGiBVOnTlWbV1Nbt24lJSWFli1b4uTkhJOTEwkJCbz99ts4OTkRFBSkdrcDfn5+NGzYkH379unfehWnfrmob169qW9uX9Qvty/qlwvYtl+uJHoFcHFxoVWrVqxatap4W1FREatWrSIuLs6GkUlFiIqKIjg42Kr909PT2bhxo9q/CjMMg8cee4xFixbx7bffEhUVZbW/VatWODs7W7X7nj17OHTokNq9mikqKiIvL09tXk116dKF7du3k5iYWLzExMTQr1+/4udq9+ovMzOT3377jZCQEP1br+LULxf1zasn9c0F1C+v7tQvF7Btv1zlXCrI2LFjGTBgADExMbRu3Zq33nqLrKwsBg0aZOvQpAxkZmayb9++4vWkpCQSExMJCAggIiKCMWPG8PLLL9OgQQOioqJ48cUXCQ0NpWfPnrYLWq7LyJEjmTt3Ll988QXe3t7F9bZ8fX1xd3fH19eXRx55hLFjxxIQEICPjw+PP/44cXFxtGnTxsbRy7V69tln6dGjBxEREWRkZDB37lzWrFnDsmXL1ObVlLe3d3E91fM8PT0JDAws3q52r37GjRvHXXfdRWRkJEePHmX8+PE4Ojryl7/8Rf/WqwH1y6s/9c3tj/rm9kf9cvujfrl9qlT9ckMqzLRp04yIiAjDxcXFaN26tbFhwwZbhyRlZPXq1QZwyTJgwADDMAyjqKjIePHFF42goCDD1dXV6NKli7Fnzx7bBi3X5XLtDRizZ88uPiYnJ8cYMWKE4e/vb3h4eBj33nuvcezYMdsFLdft4YcfNiIjIw0XFxejZs2aRpcuXYzly5cX71eb24eOHTsao0ePLl5Xu1c/DzzwgBESEmK4uLgYYWFhxgMPPGDs27eveL/avOpTv7x6U9/c/qhvbn/ULxfDUL/cHlSmfrnFMAyj7FPzIiIiIiIiIiIiIiJVn2qii4iIiIiIiIiIiIiUQEl0EREREREREREREZESKIkuIiIiIiIiIiIiIlICJdFFREREREREREREREqgJLqIiIiIiIiIiIiISAmURBcRERERERERERERKYGS6CIiIiIiIiIiIiIiJVASXURERERERERERESkBEqii4hIhbFYLHz++ee2DkNERERExK6pXy4icnWURBcRsRMDBw7EYrFcsnTv3t3WoYmIiIiI2A31y0VEqh4nWwcgIiIVp3v37syePdtqm6urq42iERERERGxT+qXi4hULRqJLiJiR1xdXQkODrZa/P39AfMnnTNnzqRHjx64u7tTt25dFi5caHX+9u3b6dy5M+7u7gQGBjJkyBAyMzOtjvnwww9p2rQprq6uhISE8Nhjj1ntP3nyJPfeey8eHh40aNCAxYsXl++LFhERERGpZNQvFxGpWpREFxGRYi+++CK9evVi27Zt9OvXj759+7Jr1y4AsrKy6NatG/7+/mzevJkFCxawcuVKq874zJkzGTlyJEOGDGH79u0sXryY+vXrW91j4sSJ9OnTh59//pnbb7+dfv36cfr06Qp9nSIiIiIilZn65SIilYvFMAzD1kGIiEj5GzhwIP/9739xc3Oz2v7cc8/x3HPPYbFYGDZsGDNnzize16ZNG1q2bMmMGTN4//33efrppzl8+DCenp4AfPXVV9x1110cPXqUoKAgwsLCGDRoEC+//PJlY7BYLLzwwgtMmjQJMD8AeHl58fXXX6sGpIiIiIjYBfXLRUSqHtVEFxGxI506dbLqjAMEBAQUP4+Li7PaFxcXR2JiIgC7du2iRYsWxR11gHbt2lFUVMSePXuwWCwcPXqULl26XDGG5s2bFz/39PTEx8eHlJSUa31JIiIiIiJVjvrlIiJVi5LoIiJ2xNPT85KfcZYVd3f3Uh3n7OxstW6xWCgqKiqPkEREREREKiX1y0VEqhbVRBcRkWIbNmy4ZL1x48YANG7cmG3btpGVlVW8f926dTg4ONCoUSO8vb2pU6cOq1atqtCYRURERESqG/XLRUQqF41EFxGxI3l5eRw/ftxqm5OTEzVq1ABgwYIFxMTE0L59ez7++GM2bdrEBx98AEC/fv0YP348AwYMYMKECZw4cYLHH3+chx56iKCgIAAmTJjAsGHDqFWrFj169CAjI4N169bx+OOPV+wLFRERERGpxNQvFxGpWpREFxGxI9988w0hISFW2xo1asTu3bsBmDhxIvPnz2fEiBGEhIQwb948mjRpAoCHhwfLli1j9OjR3HzzzXh4eNCrVy/eeOON4msNGDCA3Nxc3nzzTcaNG0eNGjXo3bt3xb1AEREREZEqQP1yEZGqxWIYhmHrIERExPYsFguLFi2iZ8+etg5FRERERMRuqV8uIlL5qCa6iIiIiIiIiIiIiEgJlEQXERERERERERERESmByrmIiIiIiIiIiIiIiJRAI9FFREREREREREREREqgJLqIiIiIiIiIiIiISAmURBcRERERERERERERKYGS6CIiIiIiIiIiIiIiJVASXURERERERERERESkBEqii4iIiIiIiIiIiIiUQEl0EREREREREREREZESKIkuIiIiIiIiIiIiIlICJdFFRERERERERERERErw/7Ewn6L2YQxkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0620 Top-1 准确率: 0.7324 Top-5 准确率: 0.9468\n",
      "val 损失: 0.9733 Top-1 准确率: 0.7349 Top-5 准确率: 0.9241\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 1.0028 Top-1 准确率: 0.7459 Top-5 准确率: 0.9599\n",
      "val 损失: 0.9596 Top-1 准确率: 0.7383 Top-5 准确率: 0.9252\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9782 Top-1 准确率: 0.7504 Top-5 准确率: 0.9640\n",
      "val 损失: 0.9526 Top-1 准确率: 0.7412 Top-5 准确率: 0.9262\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9559 Top-1 准确率: 0.7557 Top-5 准确率: 0.9679\n",
      "val 损失: 0.9316 Top-1 准确率: 0.7485 Top-5 准确率: 0.9301\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9408 Top-1 准确率: 0.7592 Top-5 准确率: 0.9712\n",
      "val 损失: 0.9461 Top-1 准确率: 0.7447 Top-5 准确率: 0.9286\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9439 Top-1 准确率: 0.7568 Top-5 准确率: 0.9732\n",
      "val 损失: 0.9388 Top-1 准确率: 0.7463 Top-5 准确率: 0.9284\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9196 Top-1 准确率: 0.7632 Top-5 准确率: 0.9740\n",
      "val 损失: 0.9633 Top-1 准确率: 0.7412 Top-5 准确率: 0.9272\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9159 Top-1 准确率: 0.7628 Top-5 准确率: 0.9758\n",
      "val 损失: 0.9461 Top-1 准确率: 0.7429 Top-5 准确率: 0.9283\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9181 Top-1 准确率: 0.7626 Top-5 准确率: 0.9765\n",
      "val 损失: 0.9518 Top-1 准确率: 0.7426 Top-5 准确率: 0.9278\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.9076 Top-1 准确率: 0.7639 Top-5 准确率: 0.9782\n",
      "val 损失: 0.9446 Top-1 准确率: 0.7464 Top-5 准确率: 0.9271\n",
      "\n",
      "训练完成，耗时 6m 35s\n",
      "最佳验证集准确率: 0.748500\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9546 Top-1 准确率: 0.7545 Top-5 准确率: 0.9690\n",
      "val 损失: 0.9359 Top-1 准确率: 0.7464 Top-5 准确率: 0.9290\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9525 Top-1 准确率: 0.7550 Top-5 准确率: 0.9719\n",
      "val 损失: 0.9363 Top-1 准确率: 0.7467 Top-5 准确率: 0.9295\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9217 Top-1 准确率: 0.7626 Top-5 准确率: 0.9740\n",
      "val 损失: 0.9395 Top-1 准确率: 0.7467 Top-5 准确率: 0.9294\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9126 Top-1 准确率: 0.7647 Top-5 准确率: 0.9746\n",
      "val 损失: 0.9408 Top-1 准确率: 0.7461 Top-5 准确率: 0.9285\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9196 Top-1 准确率: 0.7613 Top-5 准确率: 0.9766\n",
      "val 损失: 0.9449 Top-1 准确率: 0.7456 Top-5 准确率: 0.9297\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8961 Top-1 准确率: 0.7682 Top-5 准确率: 0.9778\n",
      "val 损失: 0.9288 Top-1 准确率: 0.7474 Top-5 准确率: 0.9298\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8941 Top-1 准确率: 0.7680 Top-5 准确率: 0.9797\n",
      "val 损失: 0.9266 Top-1 准确率: 0.7504 Top-5 准确率: 0.9309\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8886 Top-1 准确率: 0.7687 Top-5 准确率: 0.9787\n",
      "val 损失: 0.9423 Top-1 准确率: 0.7449 Top-5 准确率: 0.9289\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8888 Top-1 准确率: 0.7686 Top-5 准确率: 0.9810\n",
      "val 损失: 0.9418 Top-1 准确率: 0.7473 Top-5 准确率: 0.9296\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8828 Top-1 准确率: 0.7700 Top-5 准确率: 0.9805\n",
      "val 损失: 0.9418 Top-1 准确率: 0.7474 Top-5 准确率: 0.9303\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8748 Top-1 准确率: 0.7704 Top-5 准确率: 0.9823\n",
      "val 损失: 0.9509 Top-1 准确率: 0.7466 Top-5 准确率: 0.9291\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8693 Top-1 准确率: 0.7723 Top-5 准确率: 0.9828\n",
      "val 损失: 0.9236 Top-1 准确率: 0.7550 Top-5 准确率: 0.9331\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8696 Top-1 准确率: 0.7714 Top-5 准确率: 0.9833\n",
      "val 损失: 0.9516 Top-1 准确率: 0.7487 Top-5 准确率: 0.9282\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8559 Top-1 准确率: 0.7758 Top-5 准确率: 0.9842\n",
      "val 损失: 0.9420 Top-1 准确率: 0.7489 Top-5 准确率: 0.9288\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8558 Top-1 准确率: 0.7747 Top-5 准确率: 0.9840\n",
      "val 损失: 0.9451 Top-1 准确率: 0.7503 Top-5 准确率: 0.9300\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8477 Top-1 准确率: 0.7767 Top-5 准确率: 0.9851\n",
      "val 损失: 0.9533 Top-1 准确率: 0.7479 Top-5 准确率: 0.9307\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8477 Top-1 准确率: 0.7773 Top-5 准确率: 0.9856\n",
      "val 损失: 0.9480 Top-1 准确率: 0.7502 Top-5 准确率: 0.9295\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8601 Top-1 准确率: 0.7729 Top-5 准确率: 0.9856\n",
      "val 损失: 0.9595 Top-1 准确率: 0.7489 Top-5 准确率: 0.9282\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8432 Top-1 准确率: 0.7768 Top-5 准确率: 0.9863\n",
      "val 损失: 0.9530 Top-1 准确率: 0.7536 Top-5 准确率: 0.9293\n",
      "\n",
      "第 19/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8407 Top-1 准确率: 0.7767 Top-5 准确率: 0.9875\n",
      "val 损失: 0.9473 Top-1 准确率: 0.7507 Top-5 准确率: 0.9285\n",
      "\n",
      "第 20/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8350 Top-1 准确率: 0.7786 Top-5 准确率: 0.9867\n",
      "val 损失: 0.9454 Top-1 准确率: 0.7526 Top-5 准确率: 0.9302\n",
      "\n",
      "第 21/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8300 Top-1 准确率: 0.7806 Top-5 准确率: 0.9876\n",
      "val 损失: 0.9463 Top-1 准确率: 0.7538 Top-5 准确率: 0.9296\n",
      "\n",
      "第 22/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8294 Top-1 准确率: 0.7790 Top-5 准确率: 0.9883\n",
      "val 损失: 0.9534 Top-1 准确率: 0.7528 Top-5 准确率: 0.9279\n",
      "\n",
      "第 23/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8252 Top-1 准确率: 0.7804 Top-5 准确率: 0.9886\n",
      "val 损失: 0.9584 Top-1 准确率: 0.7525 Top-5 准确率: 0.9301\n",
      "\n",
      "第 24/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8244 Top-1 准确率: 0.7795 Top-5 准确率: 0.9889\n",
      "val 损失: 0.9577 Top-1 准确率: 0.7520 Top-5 准确率: 0.9268\n",
      "\n",
      "第 25/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8117 Top-1 准确率: 0.7844 Top-5 准确率: 0.9890\n",
      "val 损失: 0.9643 Top-1 准确率: 0.7511 Top-5 准确率: 0.9277\n",
      "\n",
      "第 26/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8115 Top-1 准确率: 0.7832 Top-5 准确率: 0.9894\n",
      "val 损失: 0.9662 Top-1 准确率: 0.7522 Top-5 准确率: 0.9298\n",
      "\n",
      "第 27/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8187 Top-1 准确率: 0.7811 Top-5 准确率: 0.9897\n",
      "val 损失: 0.9549 Top-1 准确率: 0.7548 Top-5 准确率: 0.9297\n",
      "\n",
      "第 28/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8099 Top-1 准确率: 0.7833 Top-5 准确率: 0.9901\n",
      "val 损失: 0.9763 Top-1 准确率: 0.7525 Top-5 准确率: 0.9245\n",
      "\n",
      "第 29/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8080 Top-1 准确率: 0.7825 Top-5 准确率: 0.9904\n",
      "val 损失: 0.9498 Top-1 准确率: 0.7533 Top-5 准确率: 0.9285\n",
      "\n",
      "训练完成，耗时 19m 53s\n",
      "最佳验证集准确率: 0.755000\n"
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, 'fixed90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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